Aminet 18/aminetcdnumber181997.iso/Aminet/dev/misc/Istar.lha/Docs/Using Istar

Text File | 1997-02-17 | 84.5 KB | 1,689 lines

USING ISTAR - FOR INFERENCE

This is a step-by-step tutorial on how to make good use of Istar, and we
start by looking at how to make use of inference.  We take real life kinds
of tasks you might want to use it for and guide you in building knowledge
bases (KBs) of various kinds, to teach you some of the features of Istar,
and also to prod your thinking in how you could use it.  But there's lots
of facilities that you will not meet in this chapter.

      Before you start here, read the sections "How do I start it?" and
"Going further" in the <Driving Istar> document.  This guide is at a higher
level: it does not describe the features of Istar so much as when you might
make use of them, for what purposes.

      We present several sections here, each a small project with Istar of
about quarter of an hour.  Each is designed to let you see what Istar could
be useful for and at the same time how to employ its facilities.  It
provides an introduction not just to use of Istar facilities but to the
creation of KBs in general.  You will find some useful tips.  The first
eight or so sections take you through some of the potential benefits of
knowledge based systems and introduce the main features.  But the KB you
build during these is far from accurate.  Only towards the end do we
consider how to make the KB more accurate.

      At the end you should be able not only to drive Istar but to take
your first steps in building real knowledge bases.  Have fun.


1.  EVALUATING STOCKS AND SHARES

Suppose your friend asked you for advice on buying stocks and shares.  As
an alternative to talking or writing to him you could give him a knowledge
base.  Then he could run it, it would ask him questions about a company he
is interested in and would offer advice.

      In this section you will create a (much simplified) knowledge base
(KB) that gives such advice.

      What other reasons are there for building such a KB?

      (a)  For yourself: it would remind you at a later date of the advice
      you were giving; so a KB can be your memory.

      (b)  This could be useful for legal reasons; so a KB can be a precise
      record of knowledge.

      (c)  You could provide it to 100 friends and acquintances, almost as
      easily as to the initial friend; a KB can be a distribution
      mechanism.

      (d)  You could sell it.

So, let's start building it.

#  Fuzzy knowledge.  Buying shares depends on 'fuzzy' factors like the
quality of the management, the strength of the market, etc. as well as on
quantitative ones like share price.  So we will use Bayesians to build our
KB - these allow accumulation of fuzzy evidence for and against various
propositions.  In the top-right window (Item Types) select 'Bayesian'.

#  Drawing an inference net.  Draw a box (Driving step 3) towards the right
of the screen and name it 'Buy It' (Driving step 8).  (Note the wee 'OK'
button to the right of the Label gadget; it is a version of the main one
further below, there for your convenience when all you are doing is to fill
in a name or meaning.)  This is the 'goal' item, the thing we want to find
out, the overall purpose of our KB.

#  Draw two more boxes a couple in inches to the left of 'Buy It', calling
them 'Will grow' and 'More profits', which express the ideas that the
company will grow in the future and that its profits are likely to
increase.

#  (Naming.  You can use longer names if you wish, but the shorter the
better.  A good rule for naming of this kind of fuzzy qualitative concept:
make the name a short proposition rather than a numeric value.  So just
'Profits' can be confusing since it could mean "Profits are likely to
increase" or "The numerical value of last year's profits".)

#  Both are good indicators that shares in the company are worth buying, so
we link them to 'Buy it'.  Draw a link from 'Will grow' to 'Buy it' and
from 'More profits' to 'Buy it'.  (Your KB should now resemble a V on its
side if the two antecedents, 'Will grow' and 'More profits', are to the
left of 'Buy it' and slightly higher and lower than it.)

#  Run it (Driving step 11) with a company in mind - fictitious or real.
It will ask you to provide slider values that represent your degree of
belief, first your belief that the company will grow, then your belief that
the company's profits will increase.  Belief is positive to the right,
negative to the left, the further from the centre, the stronger your
belief.  (Note: at this stage, it just puts the name before you.)

#  Look at the result (Driving step 12).  Not very interesting yet and
probably totally wrong in its result, but you have built your initial KB.
Not really ready for your friend, so let's add some more knowledge.

#  What determines whether a company - any company - will grow?  There are
many factors, and we'll add a few below.  But for now we'll only add two:
the company is in a growth sector of the economy and the company has good
management.  So add two antecedents to 'Will grow' (draw two boxes with
names e.g. 'Strong sector' and 'Good management', and link them to 'Will
grow').  Now a create a couple of antecedents for 'More profits' and link
them in: the company has a good financial history ('Good fin hist') and the
company has low overheads ('Low overheads').  Now the KB looks like a
branching tree on its side.

#  But most KBs are not pure trees but networks.  Connect 'Good management'
to 'More profits', showing that we believe that increases in profits are
more likely with good management.

#  Run the KB; it will now seek your degree of belief about four things:
Strong sector, Good management, Good fin hist and Low overheads.  Make some
positive, some negative.

#  Save it, by clicking the 'Save As' button on the main control panel (top
left).

#  To summarize, in this section we have:

      #  Noted several reasons for building a KB.
      #  Used bayesian attributes/items to represent fuzzy knowledge ..
      #  .. in terms of degrees of belief.
      #  Drawn an inference net to evaluate attractiveness.
      #  Run it.



2.  DECISION SUPPORT - WHAT-IFFING

A KB like the one you have just created can be used in a decision support
mode.  If we run it several times giving different answers each time then
we can see the effect of the various factors.  This is called what-iffing.
From this we might find that certain factors are more important than others
and this can help us plan e.g. where to put resources.

#  Run the KB (Driving step 11 and 12) with Strong Sector and Good fin hist
having strong positive belief (100) and Good Management and Low overheads
having strong negative belief (-100).  The result should be slightly to the
left of centre, around 33%.  (Notice that your input is in terms of degrees
of belief while the answer is in terms of probability, but visually they
should be similar at present: left of centre is negative indication, right
is positive.)

#  Now run it again (shortcut from the data panel to the action panel by
pressing the OK-Act button rather than the OK button).  Reverse the answers
and you should obtain a slightly positive indication, 66%.

#  We can derive some initial knowledge from this: the combination of Good
management and Low overheads is a stronger indication of whether to buy
than the combination of Strong sector and Good financial history.  (That
assumes our knowledge base is correct and complete, of course, which is
certainly is not!)  This is the way we use KBs for decision support: try
various combinations of factors against others.

#  But is there any overriding factor?  Run again, giving the first two 100
and the second two -100.  The result should again be slightly positive.
Then try other combinations of pairs of 100 and -100.

#  What should happen is that the pair that contains Good management should
always determine the final result.  What this means is that good management
is a more important indicator than the others.  In looking at the KB it
should be obvious why this is so:  Good management feeds its influence
through to the final goal ('Buy it') by two routes while the others feed
their influence by only one.  All other things being equal, the more routes
by which a factor feeds through to the goal the more important it becomes.

#  But in most real KBs all other things are not equal; the links
themselves have varying strengths (weights).  So you cannot determine the
strong factors merely by looking at the net; you must run it to find out.

#  (Above we always divided the factors into pairs and always gave them
extreme values; that was just to give you the idea; in reality all sorts of
values would be used and we might vary only one or two factors at a time.)

#  To summarize, in this section we have:

      #  Used the KB in what-if mode.
      #  Thereby found which factors are important.
      #  Noted the effect of parallel inference paths.



3.  MAKING IT EASIER AND TIDIER TO USE

When your KB becomes larger the above procedure becomes a bit cumbersome.
Here are a couple of things to tidy it up and make it less cumbersome.

#  First, let us put in proper question text for the degrees of belief.
What we do is to provide question text for each of the four antecedent
factors.  To do this, for each in turn do the following:  Click with the
RIGHT mouse button on the middle of the box expressing the item.  (The
attribute panel should appear; if nothing happens, it is probably that the
main easel is not active; to make it active, click on the easel with the
left mouse button as per normal Intuition practice.)  In the attribute
panel, find the first long string gadget, the 'Q' to the right of 'User
supplied'.  This is the question text.  Click in it and fill in the
question texts, e.g. "Do you believe the company is in a strong sector?",
"What is your degree of belief that the company has good management?",
"Have they a strong financial history?", "Do you believe they have low
overheads?"  Now, run the KB again, and your questions should appear.

#  Second, it's a nuisance having to bring up the attribute data panel each
time to see the result.  We can show the result directly on the main easel.
To do this bring up the attribute data panel for 'Buy it' (Driving step 8
or 12).  At the left end of the third row of gadgets is a check box 'Show
Value'.  Click that so that a tick appears.  Click Ok-Act to bring up the
action panel and run the KB again.  You should see a short black value line
part way across the bottom of the 'Buy it' box.  This shows its current
value as a probability; the longer it is, the higher the probability or
degree of belief in the concept expressed by the box.  Run it a few times
and you can see how the value changes, not so precisely as with the data
panel but enough to give a useful indication.  (Note: the show value
facility does not work for all data types at present; only booleans,
probabilities, proportions and bayesians.)

#  Such a value line is solid when the attribute is answered, and dotted
when it is unanswered.  By judicious choice of which attributes have value
lines you can gain a visual indication of how the KB is progressing.

#  Third, you can do this with all the antecedents too, showing their
values.

#  Fourth, if you just want to change a single input antecedent, you don't
have to reset and re-ask all the others.  Suppose you want to see the
effect of varying just 'Good management'.  Click on it with the left mouse
button to bring up an action panel for it.  Then hit Reset and Infer.  You
will be asked only for Good management.  But the answer will be propagated
through to 'Buy it'.  Try this several times.  Forward propagation does not
stop at the current goal (i.e. for which an action panel has been raised),
but spreads throughout the entire KB net as far as possible.  By varying
'Good management' between its extremes you can see the maximum potential
effect it can have, which is a good indication of its importance.  (Note:
For this to work properly, you should ensure that the final goal, 'Buy it',
has been answered.  If it is not answered then the effect of 'Good
management' will not be propagated through to it because propagation
usually only occurs once an attribute is answered.)

#  (There is also an override facility, by which you can do similar what-
iffing with items in the middle of a net; but we will not cover that here.)

#  A goal is an attribute whose value we are interested in obtaining, by
the backward and forward chaining process and the question sequence.  So
far, to obtain the value of a goal like 'Buy it' you have had to bring up
the attribute action panel, then hit Reset and Infer.  But suppose we have
twenty such goals; it would be tedious to do this twenty times.  As we will
see below, we can set up goal lists to make this cumbersome.

#  Not yet saleable.  Your KB is probably tidy enough to be usable by you
and by your friend if s/he is sympathetic.  But it is not yet tidy enough
to sell (even supposing that the knowledge is complete and accurate).  That
involves setting up goal lists etc. which we will not discuss here.  But
this version of Istar is not designed to take you all the way to building a
commercially attractive KB.

#  To summarize, in this section we have:

      #  Attended to ease of use and tidiness.
      #  Introduced question text.
      #  Used the Show Value facility.
      #  Inferred single attributes on their own.
      #  Used goal lists.

In some versions a simple shares advisor can be found in the KBs/Shares
file.  It is far from complete, and must not be used to give advice.  But
loading and running it could be instructive.


4.  KNOWLEDGE REFINEMENT AND CLARIFICATION

By now you have probably thought about other factors that should be taken
into account when deciding whether to purchase shares.  Also, you have
probably thought that concepts like 'Good management' are a bit vague.

      You'd be right on both accounts.  What has happened is that in the
act of putting the KB together and then trying it a few times your thinking
about the domain of knowledge has been stimulated.  This can be just
remembering things; it can also be actual refinement or clarification of
your own knowledge.  Istar can help you to clarify and refine your
knowledge, which is especially useful is decision support.

#  The first step in refinement or clarification of knowledge is to set
down precise meanings.  By 'Buy it' we mean something like "This company's
shares are worth buying at the moment."  So bring up the attribute details
panel for 'Buy it'.  Top right is a string gadget for 'Meaning'; into it
put text similar to that.

#  Important Tip:  Normally you should fill in the meaning as soon as you
create the box.  (We have done so later here because of the order in which
these tutorial sessions have been planned.)

#  (Tip:  Notice several things that have been specified when making the
meaning of 'Buy it' precise:

      #  What we buy: "shares"
      #  Which we buy:  "of this company"
      #  The situation (when, where, etc.): "at the moment".

Making meaning precise often involves asking what, when, where, who, which,
etc.)

#  Now make the meaning of 'Strong sector' precise.  What about: "The
company operates is a strong and growing sector."  Notice the inclusion of
growth as well as general strength.

#  This inclusion of growth sets us thinking: we are happy about steady,
well-founded growth, but maybe not about artificially induced boom-type
growth.  So, for now, go back to 'Strong sector' and alter its Meaning to
"... strong and growing (but steady, not boom) sector."  (Note how we are
frequently accessing the Meaning gadget, and how useful is the wee 'OK'
button.)

#  Now make the meaning of 'Low overheads' precise; devise your own text.

#  Now try 'Good management'.  This one's perhaps not so easy to define.
The two links we have from it, to 'Will grow' and 'More profits', speak of
two aspects of quality of management.  The link to 'Will grow' speaks about
the extent to which the management has a vision to grow the company and has
the skills to do so, such as marketing.  The link to 'More profits' speaks
more about the financial policies of the management: is their spending
under control, is their investment policy sound, and so on.  So, as we try
to make the meaning precise we see that perhaps there are several things
currently bound together in the single concept 'Good management'.

#  We have several options here.  We can retain the the single, composite
concept.  We can split the concept into several others.  Or we can do both.
We will do both, first splitting it and then reinstating it for usability
purposes.

#  Pull the 'Good management' box over to the left of the others, leaving
enough space to place a couple of boxes between it and the others.  The
links extend to follow it.

#  Draw a box and label it 'Vision for growth' and give it a meaning like
"The management of this company has a vision for growth."  Place it
somewhere north-east of 'Good management'.

#  Now we will redirect a link.  Hold the left shift key down.  This
enables you to 'pick up' the end of a link and move it to another box.
Place mouse cursor over the end of the link from 'Will grow' where it
leaves the 'Good management' box and press left mouse button and drag the
mouse.  The link should now leave the box and follow the mouse.  Release
the end of the link over the new 'Vision for growth' box.  (Oops:  The
wrong link (the one to More Profits) is picked up instead?  It picks up the
link nearest to the mouse cursor position.  Hit Escape and try again.
Oops:  It doesn't pick up the link but rather starts drawing a new link or
box?  You didn't keep the Left Shift key down while you pressed the LMB.
Oops:  The box grows or shrinks in size as you moved the mouse?  That
happens if you hold the Right Shift key down istead.  In either case, hit
Escape and try again.)

#  If you run the KB now you will be asked about 'Vision for growth' as
well as all the others.

#  (Note:  The ability to redirect a link so easily is a boon: in many
pieces of software you have to delete the link and draw a new one.  While
this is not much of a problem here, once you have added weights to the link
and perhaps routed it around the diagram you lose all that information and
have to reinstate it all again.)

#  Now draw a box 'Good fin policy', somewhere south east of 'Good
management', with meaning like "The management of this company has sound
financial policy."  Redirect the link between 'Good management' and 'More
profits' in the same way as above so that it now starts at 'Good fin
policy'.

#  (Notice:  We still have a 'Good management' box but it is not connected
to the goal 'Buy it'.  If you run the KB now it will no longer ask about
'Good management'; backward chaining only reaches those parts that are
connected to the goal.  We could delete it, but as there is no need to do
so and as we sometimes find we need such items later, just leave it and
ignore it.  We will come back to it at the very end, making the KB more
usable.)

#  Now that we have identified sound financial policy as a relevant concept
in our KB we notice its similarity with good financial history.  Assuming
that the management has been in place for some time, presumably the good
financial history is due in part to sound financial policy.  So we really
want 'Good fin policy' to link into 'Good fin hist' as well as directly
into 'More profits'.  Link them (drawing a line from the right hand edge of
'Good fin policy' into 'Good fin hist' - if 'Good fin policy' is above
'Good fin hist' then you might want to make the link S-shaped, using the
space bar to insert bends).

#  To come to think of it, maybe we shouldn't have 'Good fin hist' at all.
Maybe it is almost completely subsumed under 'Good fin policy' as far as
'More profits' is concerned?  So we will merely delete the link from 'Good
fin hist' and 'More profits' so that the former no longer has influence on
the latter.

#  (We have the option of at least two other courses of action: delete the
item 'Good fin hist' or merge it with 'Good fin policy'.  See the section
on Common Net Manipulations.  Deletion of an item is more drastic than
deletion of a link and in knowledge refinement it is often to err on the
side of caution.  Even though the two concepts appear similar as far as
'More profits' is concerned, they are not in fact identical in terms of
their actual meaning, and it might come about that later parts of the KB
require 'Good fin hist' as distinct from 'Good fin policy'.)

#  Deleting a link.  With right mouse button click over the link between
'Good fin hist' and 'More profits'.  Up comes a Relationship Instance panel
with details of this particular link.  (Notice, in passing, the Unary Op
type ('Normal') and the Weight figures towards the bottom which is
contained in four small boxes and should read 3,1, 1,3.)  But ignore all
else but the 'Delete' button.  Click it.  The link disappears.

#  Save (Save-As) the KB as 'R1' so we can pick it up later.

#  While we have been manipulating the concepts above, maybe you have
thought of other factors that contribute to a belief that this company's
shares are worth buying?  If so, you can always add it: just place a box
for it, link it to 'Buy it' and give it a name and meaning.  Maybe you've
thought of something extra that contributes to a belief in 'Will grow' or
any other box.  Add the new concept(s) in the same way.

#  Negative evidence.  Lastly, for now, maybe you have also thought of some
factor that would lower your belief in 'Buy it'.  For instance, even if we
believe the company will grow and its profits will increase, if there are
rumours that its management have been involved in shady dealings, then
perhaps it would be dangerous to buy shares.  So, in a suitable space to
the left of 'Buy it' draw a box 'Shady dealing' with meaning "The
management is believed to have been involved in shady deals."  Now start
drawing a link from 'Shady dealing' towards 'Buy it', but while you are
drawing it, hit the Minus key (to right of '0' on top row of keyboard).
You should see the line change colour from red to black, indicating that it
is now a negative link.  (If you change your mind, and want to change it
back, hit the Plus key.)  Then release the link over 'Buy it'.

#  Negative evidence acts in a similar way to normal evidence, except that
its effect is reversed.  When you run a KB with a negative link, as you
increase belief in the negative evidence then it decreases belief in its
consequent.  Try it in the what-iffing mode described above.

#  Click right mouse button over the negative link to obtain its
Relationship Instance panel.  Notice that the Unary Operator is 'Negate'.
For all the positive links in your KB the Unary Operator is 'Normal'.  If
you ever want to change a link from positive to negative or vice versa once
it is drawn, then bring up this panel and change its Unary Op type (click
the wee button to left of its name, as described for Inference type in
Driving step 13).  Then click 'OK'.

#  Notice how our process of knowledge refinement works, in several ways,
and of course there are others.  Istar provides an easy to use too to aid
the process of knowledge refinement and clarification.

#  To summarize, in this section we have:

      #  Found several ways of refining knowledge:
      #  .. by making meanings precise
      #  .. by splitting a concept to become more than one
      #  .. by merging two concepts that are (almost) the same
      #  .. by thinking of other concepts
      #  Noted how these happen naturally as we build the KB.
      #  Used the facility to redirect links.
      #  Deleted a link.
      #  Used negative evidence.



5.  KNOWLEDGE COMMUNICATION AND MUTUAL UNDERSTANDING

Imagine carrying out knowledge refinement with a colleague, both of you
around the screen.  To have someone to bounce ideas off is often very
fruitful.  And it is often of benefit to ensure the newly emerging ideas
are communicated to a colleague - and understood by a colleague.  Istar is
designed for partnership working, where two (or a few) people are around
the screen.

      (Note: This is NOT the same as group working where each has their own
screen linked by some electronic means, though in principle Istar could
support that.  What we are talking about here is the technically more
modest situation - but probably socially and practically more useful
situation - where two or more people sit around a single screen.)

      You have a partially developed KB up in front of you and your
colleague.  Think of the knowledge refinement steps we have been through:

      #  making meanings precise
      #  a concept becomes more than one
      #  two concepts are seen to be (almost) the same
      #  thinking of other concepts.

If a colleague sat with you, there are four things that could be going on.
The first two occur when knowledge is being refined, as above.

      #  First, your colleague just observes your actions and listens to
      you as you refine your knowledge.  Then your thinking and reasoning
      would be communicated.  S/he would understand why you believe, for
      instance, why 'Good management' is not a sufficiently precise
      concept.  Using Istar helps this communication and mutual
      understanding of what are often ill-defined areas.

      #  Second, your colleague takes an active part in the refinement
      process, as the two of you discuss whether 'Good management' should
      be split in two, in three, or kept as a whole.  Using Istar
      facilitates this discussion by providing a graphical 'language' in
      which to express and try out ideas.

The third and fourth occur when knowledge is already in a reasonably clear
state and is merely being set down into the KB without being refined.
(This might happen, for instance, when you are entering knowledge from a
rulebook or knowledge of established good practice.)

      #  Third, your colleague just observes you building the KB.  But the
      order in which you build it communicates something of importance.  So
      does your 'body language' while building it - for instance, the act
      of moving 'Good management' to the left to make room gives
      information about your intentions.  Istar is then facilitating simple
      one-way, communication, but of more than pure information.

      #  Fourth your colleague takes an active part in the construction
      process: "Haven't you forgotten X factor?", "Is that how you
      interpret that rule; I would interpret it in a different way."
      Again, Istar facilitates communication, but this time a two-way
      communication.

      In addition to its clear graphical display and intuitive mechanisms
for drawing knowledge bases, it has been found that a third facility is
very important for communication:  the flashing up of the meaning of an
item at the bottom of the screen as the mouse runs over it.  You've almost
certainly noticed it, but if not, just move the mouse over the items of
your KB.  (If nothing happens, it is probably because the main easel window
is not active, and so you must make it so by clicking over its background.)

      Superficially like the speech bubbles that come up in MUI, Windows or
the Mac, that explain what a button is for, this has a rather more
sophisticated use in showing high level meaning rather than low level
action.  It was found during the INCA project (Basden, Brown, Tetlow and
Hibberd, 1996) that the knowledge engineer (yourself) made little use of it
since it was not in the main field of view but the onlooker (your
colleague) made enormous use of it to see the meanings of the various items
of itnerest.

      To summarize, in this section we have:

      #  Noted how Istar can aid communication in four ways
      #  .. by working with a colleague
      #  .. who either actively takes part or just observes.
      #  Noted the use of Istar's facility to show meaning.



6.  TYPES OF BENEFIT OF A KB

We have now discussed four types of benefit that can accrue from a KB.  In
the first two:

      #  simple evaluation of the attractiveness of a share
      #  what-iffing to find significant factors

the benefit accrues from running a completed KB, while in the latter two,

      #  refinement and clarification of your knowledge and
      #  communication and discussion,

the benefit accrues from the process of constructing the KB, rather than
running it.  For the remaining sections we will return to types of benefit
that can accrue from running a completed KB, and how these benefits
influence the form and style of the KB:

      #  predicting behaviour
      #  selection from known options
      #  selection from undefined options
      #  causal modelling
      #  diagnosis
      #  critiquing.

All these assume a completed KB and the benefits accrue from running it.
The KB you have already built is general purpose and can fulfil most of the
purposes but with varying degrees of clumsiness.  For most effective use
the KB should be tailored to the purpose for which it is designed.

      I say purposes rather than purpose because many KBs hold knowledge
for more than one purpose.  For instance the Wheat Counsellor KB first
predicted what diseases were likely in a crop of winter wheat and then
selected the appropriate preventative treatment.  But we will look at the
style required for each purpose, and as we do, will meet and learn about
various facilities of Istar.


7.  PREDICTING THE BEHAVIOUR OF YOUR SHARE

Your knowledge has been refined and the KB now expresses what you believe
about share purchasing (or let us suppose so).  As you might have realised,
your KB can be used to predict outcomes.  Suppose you have knowledge of a
particular company and its situation.  Then if you poke this information
into the KB it will predict the attractiveness of the shares of that
company to investors.

#  All you have to do is to run the KB; there is no change required to the
knowledge.  (Bring up the action panel for 'Buy it' and click Reset and
Infer.)

#  What this underlines is that a well designed KB is actually multi-
purpose when run:  for evaluation, critique, understanding, communication
and prediction.  Just as with any good model.  So, in a sense, a good KB is
a model of reality and Istar can be seen as modelling software.  But, as
those of you who have been involved in modelling will have realised, a
rather different style of software.

#  So far you have been dealing with bayesians, representing fuzzy
concepts.  But much modelling deals with harder or more precise concepts,
and for these we need numbers, booleans, etc.  Istar provides a host of
these (though the current version as of June 1996) is still under
development.  For the full list, see the file Value Types.  For now we will
use numbers and booleans.

#  A boolean is like a sharp, hard bayesian, the common true-false, yes-no
distinction.  Bring up the attribute details panel for 'Strong sector'.  On
the second row of gadgets, headed 'Value', the leftmost shows the value
type (Bayesian).  Click on the wee type-change gadget at its left-hand end
and a list of value types appears.  Select Boolean (and hit its OK button).
(If you get a warning message that value type is inconsistent with
inference method, it just means that 'Strong sector' is set to 'Infer'
rather than 'User supplied'; ignore it for now as it will have no effect
since there are no antecedents.)  You will see the value type in the
attribute detail panel change - but it has not come into effect yet.  So
click 'OK'.  Then bring up the attribute detail panel again, and you will
see that the value gadget itself, to the right of the value type
identifier, is no longer a slider but a checkbox.  You have changed value
type.

#  Now run it, and you will see that when it asks about 'Strong sector' it
no longer gives you a slider but a check box.  (Rather ugly, this tiny
checkbox; in future versions there will be an option of a larger gadget
saying Yes/No.)

#  Now for a numeric value.  We'll use floating point numbers here, though
there are other types including integers, ratios, ordinals, enumerators,
proportions, etc.  First, we'll practice with just numeric values, then
we'll use a numeric value in our bayesian network, which takes a bit more
thought.  Because: how can we integrate a numeric concept like Share Price
into a fuzzy propositional concept like whether or not to buy the share?
Think about it as we practice some numerics.

#  Distance is half of acceleration times the square of time (if I remember
my school physics correctly).  On the Select Item Type panel click on
Float.

      1.  In a clear part of the easel (notice how it smoothly scrolls as
      you move the mouse - a wonderful feature of the Amiga!) lay down an
      item and label it 'Distance'.  This will be our goal.

      2.  Then lay down three antecedents to the left of it: 'Time',
      'Acceleration' and 'Constant'.

      3.  Link all of them to 'Distance'.

      4.  Bring up the attribute details panel for 'Distance' and locate
      the Inference Method type gadget.  It should lie just to the right of
      the 'Infer' radio button and should say "X = A + B + C .." or
      something similar, meaning that the value of this attribute is
      calculated by adding together the values of all the antecedents.  We
      want multiplication, so click on the wee type-change button at its
      left hand end and select "X = A * B * C .." from its list.  Click OK.

      5.  But we want the square of time, not time itself: draw a second
      link from 'Time' to 'Distance', putting a bend in it (Driving step 5)
      to visually distinguish it from the first one; having two links gives
      the square of distance, having three will give the cube, etc.

      6.  Bring up the attribute details panel for 'Constant', set the
      derivation radio button to 'Const' and set the value to '0.5', click
      its OK button.

#  What you have is a wee inference net that says the result is the product
of Time, Time, Acceleration and the constant, 0.5.  Bring up the action
panel.  Run it, giving a time of 2 and an acceleration of 3, say.  The
result should appear as 6.  With this you can predict how far a stone will
fall from the top of a tower block in a given time.  (Or, as mentioned in
the first paragraph, you can put this to other uses such as evaluating
whether the stone will reach the 23rd floor in three seconds, such as doing
a what-if on different strengths of gravity, such as refining your
knowledge of what factors contribute to distance fallen.)

#  Now, how can we link a numeric concept into a bayesian network?  The
answer is that we cannot meaningfully link it in directly.  Instead, we
must often compare the numeric attribute with something and emerge with a
truth statement.  So, for instance, we could say that if the Share Price is
going up (greater than it was a week ago) then we predict that the share is
worth buying.  So let's put that in.  (I know that knowledge is wrong; you
can refine it below!)  It takes several stages, since you will learn and
use several new features.

#  First, place two Float boxes south west of 'Buy it' with enough space
for another between them.  Label them 'Share Price' and 'Price last week',
giving meanings of "Current share price of this company" and "The share
price of the company a week ago".  If you like, give them suitable question
text.

#  Now select Boolean item type, draw an item between 'Buy it' and these
two.  Label it 'Shares Rising' with a meaning "The share price is rising".

#  Now link 'Share Price' as antecedent to 'Shares Rising'.  Then link
'Price last week' similarly.  Do it in that order.

#  Bring up the attribute details panel for 'Shares Rising'.  Change the
inference method to "Whether A > all".  What this inference method does is
to see whether the first antecedent is greater than all the rest (that is
why the order was important).  It returns a boolean result (unlike most
inference methods, in which the consequent is of the same value type as the
antecedents).  Click OK.

#  (If you wish to see this in operation, run the wee KB in which 'Shares
Rising' is the goal, giving various pairs of values for 'Share Price' and
'Price last week'.)

#  Now link 'Shares Rising' as antecedent into 'Buy it'.  Strictly, the
bayesian accumulation inference method (which 'Buy it' has) needs bayesian
actecedents.  But for your convenience Istar is tolerant, making automatic
conversions from probability and boolean.  (If the antecedent is a simple
probability it has no a-priori and this is assumed to be half (0.5, 50%).
If the antecedent is boolean, as here, then it is treated as a probability
with a value of either 0 or 100% with an a-priori of 50%.  For details of
a-priori values, see below.)  You will find automatic value type conversion
quite frequently, for instance allowing you to mix integers and floats when
doing arithmetic.

#  Now run the whole KB, and see the effect of changing share price
compared with price last week.

#  Knowledge refinement:  You probably disagree with the idea that shares
should be bought when their price is rising.  Many will say they should be
bought when price is falling and sold when rising.  That simple rule was at
the centre the stock market crash of 1989, so I thought we'd put in its
opposite here!  You are free to correct my possibly wrong knowledge to the
conventional rule, and there are several ways of doing it:  (a) alter
inference method from "Whether A > the rest" to "Whether A < the rest" (and
alter the label and meaning of 'Shares rising' in line with that).  (b)
(easier:) make 'Shares rising' negative evidence for 'Buy it', by altering
the unary operator of its link to 'Negate'.  Probably, the real knowledge
of the link between share price movement and whether to buy is more
complex, depending on the rate of price rise or drop, how long it has been
rising or dropping, and what other dealers are doing.  It would be a good
exercise to try to work out a small knowledge base for this, now you have
the ability to connect numeric and bayesian information.

      In this section we have:

      #  noted the use of a KB for prediction as well as others
      #  made use of numeric information
      #  found one way of linking numeric and bayesian
      #  noted there are two ways of negating knowledge.


8.  SELECTION: DECIDING WHICH SHARE TO PURCHASE

Istar can be used to select options, especially where the selection
criteria are fuzzy and make use of human 'judgement'.  This section looks
at one way of doing this.

      So far, our KB is run for a single company, and we have been
evaluating whether its shares are, or predicting whether it shares will be,
worth buying, but we can also use it to select the best company from which
to buy shares.  To use our existing KB we must run it several times, once
for each company, and remember the result for each one.  That is, the
general purpose KB we have constructed can also be used for selection.

      But running it for each company can be inconvenient.  For a start, we
need to remember the result for each.  To continue, there are situations
where there is common information and we find we are having to enter the
same information each time.  This section looks at one way of allowing
several selections in a single KB.

      This method would not normally be used for selecting between such
varied things as company shares, but mainly for selection from a small and
static range of options.  For instance, to select the best of five alloys
from which to make machine parts, depending on their properties.  But we
will continue with our shares KB and assume we have two companies between
which we must make a decision: Acme plc and Bloggs Ltd.  Both are in the
Information Technology sector.

      What we must do is to create separate parts of the KB for each, one
part for Acme, one for Bloggs, but using as much common knowledge as
possible.  (Current version of Istar does not have a knowledge duplication
facility, so we must do it manually.)

#  Load the KB you saved as 'R1'.

#  First, let's rename the attributes in the existing KB as relevant to
Acme.  Easiest way to do this in our present version (few attributes) is to
bring up the attribute details for each attribute in turn and put an "A:"
(for Acme) in front of both label and meaning.  Do so - except for 'Buy it'
which is better labelled 'Buy Acme' and for 'Strong sector' which is common
data and thus not specific to either.  You can ignore the dangling 'Good
fin hist'.

#  Now we build a similar KB for Bloggs, but without 'Strong  Sector'.
Preferably underneath the current one.  Create items for:  'Buy Bloggs',
'B: Will grow', 'B: Vision for growth', 'B: More profits', 'B: Good fin
policy', (but not for 'Strong sector'), and link them in the same pattern
as for Acme.  (Don't bother duplicating 'Good fin hist'.)  For 'Buy Bloggs'
hit the Show Value button on the Attribute Details panel, so, like 'Buy
Acme', the value is shown as a line in the box.

#  Now link the common 'Strong sector' above Acme into 'B: Will grow'.
'Strong sector' is common to both because both are in the same sector, I.T.
(Note the long almost-vertical link you have created: this is a visual cue
in the diagram that says 'this link is different': unlike most, it is
common to the two parts of the KB.)

#  Make 'Strong sector' the first antecedent of 'B: Will grow', as
described in the following paragraphs ...

#  (A note about order of antecedents.  Bring up the attribute panel for
'A: Will grow' and look at the bottom left corner List of Antecedents.
This tells you which items/attributes are the direct antecedents to this
one.  Notice that 'Strong sector' is first.  Now bring up that for 'B: Will
grow' (no need to send 'A: Will grow' away; Istar allows several such
panels to be up at the same time).  'Strong sector' should appear as second
in the list.  This is because we linked 'Strong sector' to 'B: Will grow'
after we linked the rest, not before; any new link is added at the end.
What this means is that if you run just 'Buy Acme' you will be asked about
sector strength first, while if you run just 'Buy Bloggs' sector strength
is asked second.  Normally this doesn't matter much because the order
should not affect the results (except for some order-sensitive inference
methods like "Whether A > the rest").  The main concern is over usability;
the user - your friend of the first section, perhaps - might wonder why the
two parts behave differently and whether this has any significance.  The
attribute details panel allows you to change the order by selecting an
antecedent and making it first.  So select 'Strong sector' on the list (by
hitting it with the LMB to highlight it) and click the nearby 'To 1'
button.  It should jump to first in list.  And if you run 'Buy Bloggs' now
it should ask about sector strength first.)

#  (The other buttons by the antecedent list - See, R, X - give you
information.  'See' takes you to the named antecedent, bringing up its
attribute details panel.  'R' takes you to the Relationship Instance panel
for the relationship.  In both cases the original panel stays.  'X' brings
up a textual explanation of what goes on in the inference relationship -
but in the current version it is only 90% correct since it is still being
implemented.  Try them, sending each away when you have seen them.)

#  Run both parts of the KB (Acme and Bloggs) by bringing up the Action
panels for both 'Buy Acme' and 'Buy Bloggs' (you can have any number of
attribute panels up at one time).  Hit Reset and Infer for Acme, and answer
the questions that come up.  Hit Reset and Infer for Bloggs, and answer the
questions that come up.

#  Now look at the two value lines for 'Buy Acme' and 'Buy Bloggs'.  (Oops:
No value line?  Bring up the Attribute Details panel for each and ensure
the Show Value button is ticked.)  Notice how useful these value lines are
for comparing two or more similar attributes - the longer one is the share
to buy.  If you move the boxes so that they are aligned vertically above
each other, then comparison is fast and easy.  (But it only works for
bayesians, booleans, probabilities and proportions at present, not integers
nor floats.)

#  Goal lists.  But notice also how cumbersome was the process of bringing
up all the attribute action panels individually; also, you probably noticed
that you were asked twice about the common factor, 'Strong market'.  It
gets worse the larger the number of goals you have.  But we can make it
easier by using goal lists.  Bring up the KB panel and look down the second
column of buttons - hit the one labelled 'Goal List'.  A Goal List panel
appears down the bottom right of the control screen; it is empty at the
moment.  Now bring up the Action panel for 'Buy Acme' (if not already up)
and hit the button labelled 'Add Goal'.  As you can see, this adds 'Buy
Acme' to the goal list.  Now do the same for 'Buy Bloggs'.  (Notice how the
latest goal to be added is always placed at the start of the list.  You can
rearrange the order of the goal list by selecting one and hitting 'To 1',
which makes that one first.  Make Acme first if you wish.)

#  Running with a goal list.  To run with a goal list is easy.  Bring up
the KB panel and hit ResetGoals and then InfeGoals buttons.  This will
start a multi-goal inference cycle, asking all the questions relevant to
all the goals until all are answered.  It should ask about strong sector
only once, but all the other information separately.

#  (To remove a goal from the goal list, bring up the goal list, select the
goal and hit Remove.)

#  (To find a goal in a large KB, select it and hit Show button.  Then look
at the easel: everything has disappeared except the goal and the attributes
it is connected to.  To bring the easel back, make sure it is active and
hit the Numeric Enter key (bottom right on keyboard).)

#  Here is another method of showing the result when you are comparing two
or more goals like 'Buy Acme' and 'Buy Bloggs'.  It is more suited to
numeric goals which cannot be shown by the horizontal value line.  We add
some extra inference net which compares the two and provides the name of
the best goal.  In doing so you will learn a new value type and couple more
facilities of Istar.

#  On the Select Item Type panel, towards the bottom select 'Block'.  The
Free Block value type means that the attribute contains the DSAP (data
structure area pointer, the reference number) of a block of data in the KB.
Place such an attribute/item to the right of the two goals, 'Buy Acme' and
'Buy Bloggs'.  (Label: 'Best share', meaning e.g.: "This holds holds the
DSAP of the block of the share which is most attractive".)  Link them to it
as its attributes.

#  Bring up the attribute detail panel, and the inference method should be,
by default, "Which Max".  (If not, then change it.)  "Which Max" looks to
find which of the antecedents has the highest value and returns an
identifier to show which.  In our case the result is a DSAP of a block, but
it can be an index number if the consequent is integer or ordinal; see the
<Inference> file for more details.

#  (If you run it using 'Best share' as the goal - ignore the goal list,
bring up its action panel and click Reset and Infer - then the result will
be a number like 23720.  Now if you bring up the attribute details panels
for 'Buy Acme' and 'Buy Bloggs' you will see over the right hand side,
about 2/3 of the way down, a gadget 'DSAP' and one of them should be the
number you have just seen.  Rather meaningless since it is an internal
identifier.)

#  Now to convert this Block value (DSAP) into something more meaningful:
the name of the best share.  In the Select Item Type panel, select 'Free
String'.  Place a string item to the right of 'Best share' and link 'Best
share' into it as antecedent.  (You can call the new item 'Best share' if
you like as Istar allows duplicate names, but it's probably better to
differentiate it as a string version either by a different label or in the
meaning.)

#  (Notice how the colours of the labels in the various items/attributes is
a clue to the value type held.)

#  Now run the KB with this string 'Best share (string)' as goal, and look
at its result.  It should contain as its value either of the strings, 'Buy
Acme' or 'Buy Bloggs'.  This string value can then be placed in a document
(though we will not do so here; in the current version of Istar this
facility is not available).

#  Which is the better method to show which share to buy?  It depends.  But
the former (value lines) has two advantages.  One is that it is more
immediately graphical: look for the longest line.  But, more importantly,
suppose both results were low (e.g. 3% and 9%).  Then the second method
would simply say 'Buy Bloggs', whereas in reality it would probably be
inappropriate to buy either of them.  The second method, using "Which Max"
inference, makes the decision for the user while the first method merely
provides decision support for the user.  This is often more useful since if
two or more options have approximately the same value (e.g. 85%, 87%, 82%)
then it might be appropriate to choose one that is not numerically the
highest, for extraneous reasons.

#  Let's review what we've done.  We have a KB by which the user can choose
between Acme and Bloggs shares, given information about both companies and
the strength of the market.  The KB is identical for each.  But in many
selector KBs there will be small variations between the knowledge.

#  Suppose Acme is a small company and Bloggs a large one, and the user
might have a preference for large or small companies.  Select Bayesian
again and create an item labelled 'Prefers small', meaning "I prefer small
company shares".  (Notice that this is information pertaining to the user,
not to the companies or their situation.  Perfectly valid.)  Then link it
to 'Buy Acme' with a positive link and to 'Buy Bloggs' with a negative
link.  From previous work both 'Buy Acme' and 'Buy Bloggs' will be answered
and their values shown as horizontal lines in their boxes.  (If not, make
sure they are.)  Then bring up the action panel for 'Prefers small' and
reset/infer it several times with various values.  Depending on the other
information, you should see the balance between the two shares change as
you change preference.

#  In a selector KB, which has knowledge on how to select between a small,
static number of known options (like the alloys above) you will find some
knowledge that is duplicated across the options, some that is common to all
(like 'Strong sector') and some that differentiates between the options
(like 'Prefers small').  This is a general pattern.

#  To summarize, in this section, we have (not in this order):

      #  Set up a selector KB
      #  Noted that some knowledge must be duplicated, ..
      #  .. but that some is common
      #  .. and some differentiates
      #  In this case, the latter is info about the user
      #  Noted how backward chaining lends intelligence
      #  Dealt with the order of antecedents
      #  Used graphical comparison of results
      #  Used textual methods
      #  Used the Block value type with automatic conversion to name
      #  Used a selector inference method, "Which Max"
      #  Discussed methods of comparison and selection.


9.  SELECTION: DECIDING WHAT CRITERIA TO JUDGE THEM ON

The previous section looked at the construction of a KB to select between a
small number of known options, such as alloys.  In company shares we do not
have a small number of known options, but rather a large number of options
which are not known until we run the KB.  That is, between yesterday's run
and today's five new companies might have come into existence and another
twelve might have gone bust.  In such domains it is better to take a
different approach to selection.  I also found that this approach was
needed when selecting tree species suitable for planting on a particular
plot of land.

      The approach we take here is not to hold knowledge of each and every
option in the KB, but rather to use the KB to decide what selection
criteria should be used.  Then these selection criteria can be applied to a
database containing thousands of options (companies, tree species, etc.)
and the best few examined more closely.

      "But why not just use the database direct and apply a SQL query?" you
may ask.  The answer is that with each run a different set of selection
criteria must be used, so that if we just used SQL direct we would have to
write a complex SQL statement each and every run.  In effect, the KB holds
the knowledge on how to write the SQL statement needed for each run.
(Though we will not attempt that in this section, one could do so with the
current version of Istar, using string value types and the "Concat"
inference method).

      Also - and this is what we found in the tree selector KB - simple SQL
or database access is not enough.  It is better if the KB itself can
perform inference on data values obtained from the KB.  SQL etc. do not
normally have bayesian mechanisms built into them.

      This kind of KB is rather more sophisticated in structure (as well as
in detail) than the ones above.  In the ones above, three kinds of
information came together into the eventual goals:

      a)  about the requirements of the user
      b)  about the general situation, common to all options
      c)  about each specific option.

In this kind of selector, they separate out to some extent.  First we use
(a) and some of (b) to arrive at a set of selection criteria, then we apply
these to (c) and some more of (b) to find the relative attractiveness of
each of the options.  In this kind of selector, (c) is often held in the
database, but inferences from it must be made in the KB (not just in SQL)
as discussed above.

      We will build a small KB for the first phase of a tree species
selector.  It will first be a simple KB using facilities we have already
met above.  Then we will refine, learning new facilities and approaches as
we do so.

#  Move the easel so that the whole screen is empty.

#  Select bayesian item type.

#  The goals.  Lay down, to the right, boxes for three selection criteria:

      #  'High timber yield',
      #  'Hardy',
      #  'Good cover',

with meanings like:

      #  "It is important that trees have a good yield of timber",
      #  "It is important that trees are hardy and can stand vandalism",
      #  "It is important that trees provide good ground cover for wildlife
      and game."

These are our three criterion for selection of tree species.  Click the
Show Value box for each so that we can see easily the importance of each
criterion.

(Tip:  Might be useful to shorten the meanings so that more than the common
part ("It is important that trees") appear in the mouse-position window.
For instance, make the meanings like "Trees must have a good yield of
timber.")

#  Over the left hand side, place boxes to represent the requirements of
the user:

      #  'Financial return'
      #  'Timber'
      #  'Wildlife'
      #  'Near housing'

with meanings:

      #  "Planting is for financial reasons"
      #  "The income is to be from timber"
      #  "The planting should be good for wildlife"
      #  "The planting is near housing".

Notice that 'Near housing' is part of (b) above, while the others are part
of (a).

#  In the middle, place a box:

      #  'Game'

with the meaning:

      #  "The planting should support game".

#  Link 'Financial return' to both 'High timber yield' and 'Game', because
a money can be made either out of timber or game (pheasant shooting, etc.).

#  Link 'Timber' to 'High timber yield' with a positive link and to 'Game'
with a negative link.  This means that if they don't want timber but do
want a financial yield then it must be through game.

#  Link both 'Wildlife' and 'Game' to 'Good cover'.  Both require good
cover.

#  Link 'Housing nearby' to 'Hardy'.  This is because if there is housing
nearby then children etc. will visit the wood and are likely to do some
damage to the trees, so they must be hardy.

#  Now clear the goal list and put the three right hand criteria into it.
(See above for how to do this if you've forgotten.)

#  Run it (ResetGoals, InferGoals on KB panel).  That is our basic tree
selector KB.

#  (Its results show how much importance should be attached to each
criterion when assessing each tree species.  Normally (i.e. apart from
what-iffing) the KB would be run once to obtain such importances and then
these would be applied to the data for all the tree species.  The algorithm
for applying criterion weights to data will depend on need and data
available, but might consist of multiplying each degree of importance by
the appropriate data and adding together.  For instance, if 'Hardy' turns
out to be high (90%) and 'High timber yield' low (10%), then a tree with a
hardiness factor of 8 out of 10 and a yield factor of 2 out of 10 the total
would be 8 * 90% + 2 * 10% = 7.2 + 0.2 = 7.4.  For a tree with a hardiness
factor of 2 and a yield factor of 8 the result would be 2 * 90% + 8 * 10% =
1.8 + 0.8 = 2.6.  So the former tree species would be preferred.  If
running the KB had resulted in the opposite weights being given then the
tree preferences would be reversed.)

#  This KB demonstrates basically how to set up this type of selector: have
a group of items that represent the selection criteria, a group that
represent the user requirements and a group that represent the common
situation, and link them together.

#  Did you notice that even if you said you did not want a financial return
it still asked you whether the income should be from timber or not.  (If
you didn't notice it, run it again, answering -100 to 'Financial return'.)
This is obviously not right.  So we will use a couple of facilities of
Istar to amend it.

#  First, if the demand for financial return is low then there is little
point in asking about 'Timber'.  Istar offers a cut-off on bayesians; you
might have noticed the two gadgets on the bayesian attribute detail panel
called LCO and UCO.  These are lower and upper cut-off, and when zero they
are ignored.  But when not zero they come into play.  Take the lower cut-
off, LCO.  If LCO is 30% then as soon as it is certain that the probability
value of the attribute cannot exceed 30% then the attribute is considered
answered and no more of its antecedents are sought by backward chaining.
This can be used to stop questions about 'Timber' when it is known that
there is no financial interest.  Conversely with the upper cut-off, UCO.

#  Set the LCO of 'High yield' to 30%.

#  To use the cut-offs effectively we need to alter the weights of
evidence.  So far we have used mild weights of 3/1 and 1/3, symmetrical
about unity.  We need to give the link a strong asymmetry to pull the
consequent down to near zero when the antecedent ('Financial return') is
low.  Then, whatever value the other antecedent takes it will never rise
above the LCO and so they will not be asked.  (For an understanding of
weights, see the section in <Inference> on bayesians.)

#  Bring up the relationship instance panel for the link between 'Financial
return' and 'High yield'.  It has four integers in four boxes called
'Weight', and they should be 3/1 and 1/3.  Set the fourth box to 30 rather
than 3.  (Also, ensure that the unary operator is 'Normal', not 'Negate'.)

#  Now run the KB from just 'High yield' and answer 'Financial return' with
-100.  It should now not ask about 'Timber'.  (If it does, perhaps you have
run it by clicking Reset Goals and Infer Goals, because 'Timber' is still
needed for 'Game', or perhaps you have not set the LCO of 'High yield' to
30%.)

#  To stop bayesian questioning we must therefore do two things: set the
LCO (or occasionally the UCO) and set the weights on the link from the
controlling antecedent.  So we must also do this for 'Game'.  Set its LCO
also to 30% and set the weight on the link between 'Financial return' and
'Game' to 3/1, 1/30.  Then, when we ResetGoals and InferGoals, 'Timber'
should only be asked if the 'Financial return' is answered more positively
than about -30.

#  To summarize, in this section we have:

      #  Built a KB to assign weights to selection criteria
      #  .. which can then be applied to a database of options
      #  Recognised some different classes of information
      #  Found one way of preventing irrelevant questions being asked
      #  .. by employing lower (or upper) cut-off on a bayesian
      #  .. and by setting the weight of evidence on a link.


10.  DEPENDENT ATTRIBUTES - IRRELEVANT QUESTIONS

It often happens that one attribute is a sub-type of another, or is somehow
dependent on it, so that if the other is answered false then the dependent
attribute is no longer applicable.  In the example above, 'High timber
yield' is dependent on 'Financial return' in this way: if the user does not
want a financial return then high timber yield becomes irrelevant, 'not
applicable'.  When this happens, it is inappropriate to ask questions that
solely establish the value of the dependent attribute; we need to suppress
such irrelevant questions.

      In the above example we did this by means of the Lower Cut Off.  But
this is specific to Bayesian attributes.  There are two other methods.  The
second method works only with booleans and an AND inference method.  The
third, the most general, is only available in version 1.04 and later, and
works with any consequent though it requires a boolean antecedent.

#  Preventing irrelevant questions by boolean AND.  Change the attribute
type of 'Financial Return' and of 'High timber yield' to boolean (bring up
attribute details panel for each and alter the type gadget).  Also alter
the inference method of 'High timber yield' to 'A & B & ..'.  Ensure that
'Financial return' is the first antecedent of 'High timber yield'.  Run it
several times.  When you answer 'Financial return' with 'No' it should not
ask about 'Timber'; when you answer 'Yes' it should.

#  Using the CONTROL Unary Operator (Version 1.04 onwards).  Change 'High
timber yield' back to a Bayesian attribute and its inference method back to
Bayesian.  Turn off the Lower Cut Off, if active, by dragging it back to
zero.  Keep 'Financial return' as boolean.  Now bring up the relationship
details panel for the link between them, and change its Unary Operator to
'CONTROL'.  Now run the KB.  Answering 'Financial return' with 'No' should
set the consequent of the CONTROL link ('Timber') to answered and
(probably) unknown and thus suppress any further attempts to find its
value.  When you answer with 'Yes' its value will be sought in the normal
way.  This method will work with any type of consequent.

      To see the usefulness of the CONTROL unary operator, try the
following without it.  The cost of a house includes a number of optional
factors, such as a conservatory.  If the conservatory is wanted, then its
cost needs to be worked out (e.g. from its size and quality) and added to
that of the house.  But if not wanted, then don't bother asking size and
quality of conservatory, and don't add it to the cost of the house.  Using
the CONTROL unary operator, we simply link 'Want Conservatory' to
'Conservatory cost'.  Without it, the latter will probably have to employ a
Chooser, to choose between zero and a worked out cost.  While not a
complicated thing to do, it nevertheless complicates the KB diagram by at
least two extra superfluous attributes, and if there are many such
instances, the whole loses its clarity.


11.  CAUSAL MODELS

Istar can be used to built certain types of causal model.  A causal model
tries to express what happens in some limited area of activity, such as
machinery, electronics, biological systems, the weather, social systems,
history and many more.  Some are deterministic, some normative (that is,
there are laws that pertain but entities are not forced to obey them).
With a causal model we can predict what will happen, and a major use is
simulation (e.g. simulate the weather, or simulate an electronic circuit to
see whether it will malfunction, or simulate social development).

      The more normative, the more 'fuzzy' the information about them and
less accurately can we predict or simulate.  Determinative models can often
make use of numeric and boolean information while bayesians and
probabilities are more useful for normative models.

      As you might have realised, the predictive KB above was a simple kind
of causal model.  The causality was economic and logical in nature rather
than physical, and perhaps social and ethical, but it was still a valid
kind of causality.  There are grounds in philosophy for seeing logical
entailment as a form of causality.  In the same way evaluation and
selection can involve causality.  That is why the same KB could be used,
with minor alterations, for all these purposes.

      But there one purpose of a KB that does not normally make use of a
causal model - diagnosis.


12.  DIAGNOSING WHY IT WENT WRONG OR RIGHT

Diagnosis is finding out what went wrong.  Or, more generally, what some
initial state was that resulted in the observed final state.

      In most of the uses for KBs we have looked at so far (evaluation,
understanding, prediction, etc.) the inference has followed and modelled
the causality, so that input information is of some initial state or cause
and output information is of an outcome.  But in diagnosis inference and
causality flow in opposite directions, so that input information is of the
outcome and output information is about the initial state or cause.  This
gives a diagnostic KB a different flavour, though it employs exactly the
same inference mechanisms.

      We will construct a small KB that seeks to explain why a certain
company share did not give us good profits, why it went down in value when
we expected it to go up.

      First, note that the causal KB we have used so far could in principle
be used for diagnosis.  To do so we would try all combinations of input
information and see which ones gave the state we now observe of an
unattractive share.  (Try it if you like: run it several times and see
which combinations give low belief that we should 'Buy it'.)  But there are
two problems:

      a)  it is cumbersome and time consuming (many combinations)

      b)  it cannot be very specific, in that there are several reasons why
      we are not advised to 'Buy it', and we do not know which was the one
      that functioned in this instance.

      So we need to build a different type of KB, in which the goal
attributes on the right hand side are such things as 'Was not strong
sector', 'Management had no vision', etc. and the input information on the
left hand side includes what was our goal above (negated) 'Should not have
bought it' and other information which we have not represented above.

#  The first thing we can do is to try reversing the KB we have.  Load
'R1'.  Rather than reverse the actual KB we will create another below it,
reversed.  Move the easel down so there is space below it but so that the
original KB can be seen.  Squash some of its items up a bit to make room
(and also to ensure that the main easel is active).

#  Move the mouse over 'Buy it' and press the 'T' key.  This selects item
type to be the same type as the one under the mouse.  It's usually more
convenient than using the select item type panel, and here we will make
good use of it.

#  Place an item to the left hand side, corresponding to 'Buy it', but
label it something like 'ShdNotHaveBought' with meaning like "We should not
have bought this share".

#  Now select the type for 'Will' using the 'T' key, and place a box to the
right of 'ShdNotHaveBought' and above, labelled 'DidNotGrow' with suitable
meaning.  (Of course, in this KB most items are free bayesians, so strictly
we don't have to keep using the 'T' key.  But it is a good habit to get
into.)

#  Do similarly for 'More Profits', perhaps labelling it 'ProfitsFell'.

#  (Tip:  Notice the different style of labelling, without spaces; because
the labels seem to be longer, but it could be useful as a visual way of
distinguishing between casual and diagnostic knowledge.)

#  Link 'ShdNotHaveBought' as antecedent to both these.  (Note: antecedence
and consequence is reversed.)

#  Then do similarly for all other items/attributes in the original KB,
creating one below it that is the mirror image of it.

#  Now, what do you notice about this KB?  It has only one input variable,
from which all the others are derived.  So, however it is answered, there
will be no real way of distinguishing between what are now the inference
goals.  We cannot tell whether it was lack of vision or weakness of the
sector or any other factor that meant the shares did not do well.  We must
add some further information to help us do so.

#  One way to do this is to focus on one of the factors that could have
been a cause, such as 'SectorWasWeak' and ask "What else would weakness in
the sector have led to?"  For instance, if the sector as a whole was weak
then other firms in that sector would also have been weak.  So, if other
firms also did badly then this is evidence that sector weakness was the
problem, but if other firms did well then this is unlikely to have been the
problem.

#  Add a bayesian 'OthersDidBadlyToo' as antecedent of 'SectorWasWeak'.

#  Maybe you also want to set the LCO of 'SectorWasWeak' then on its
attribute list select 'DidNotGrow' and click the 'R' button to bring up the
relationship instance panel.  Set the fourth weight figure to 60.  This
will prevent 'OthersDidBadlyToo' from being asked if in fact the company
did grow.

#  In the same way, ask "What else would have happened if the company did
not grow?"  e.g. The turnover would have stayed the same, or the number of
employees would have stayed the same or reduced.  Take one or both of these
as evidence for 'DidNotGrow' in the same way.

#  Note that instead of asking about belief in the negative statement
'TurnoverDidNotIncrease' you could ask the positive statement
'TurnoverIncreased' and link it to 'DidNotGrow' as negative evidence (that
is, with a unary operator of Negate).  You will come across many situations
where it might be better to reverse a proposition and thus all its
relationship (both antecedent and consequent).

#  (Knowledge refinement:  Instead of asking about turnover and employees
separately, perhaps what we want is simply to ask "Did the company in fact
grow during this period?")

#  In the same way, asking "What else could xxx have led to?", and linking
what you come up with as antecedence for xxx, you can build a diagnostic
KB.  Be sensitive to the possibility that one of these factors might be
caused by several factors in your KB, in which case it should be linked as
antecedent to them all.

#  To summarize, in this section we have:

      #  Noted the different nature of diagnostic knowledge.
      #  Reversed a KB.
      #  Used the quick same type facility ('T' key)
      #  Enriched our KB using the What else question.
      #  Noted that it might be useful to reverse a proposition.



13.  MAKING YOUR KB MORE ACCURATE

So far most of the links in your KB have had the same weight and the a-
priori settings have remained at 50%.  This is obviously not true to real
life.  Some factors are more important than others.  In this section we
start to tailor some of the weights (strengths) of links and set the a-
priori probabilities of your bayesian attributes.

#  The a-priori probability is the foundation of bayesian evidence.  It
forms the base point, to which to add evidence as it is collected.  It is
the starting belief in the proposition represented by the bayesian, the
belief you would have in it if you could obtain no evidence one way or
another.  Usually the a-priori is the statistical probability of the
proposition being true for the class of situations in which you will use
the KB.

#  Load the 'R1' KB that you saved earlier.

#  Bring up the 'Buy it' attribute details panel.  Given 100 different
companies, on average, for how many of them would you consider their shares
attractive?  10%?  30%?  50%?  70%?  Suppose your answer were 20%, then
find the 'AP' slider gadget to the right of the main value and move it to
20.  You have set the a-priori.

#  Now ask yourself the equivalent question ("Given 100 different
companies, on average how many of them will grow?" etc.) for all other
bayesians in the KB, and adjust their a-prioris to suit.

#  It's basically a simple operation to set the a-priori and with a little
practice it becomes almost second nature.  But there can be difficulties,
especially when you start off.

#  One is that you start to think, "Well, it depends ..."  On what?  One is
that it might depend on sector.  If you can identify a factor on which this
average depends then that factor is actually a piece of evidence that
should be incorporated into your KB.  It depends on sector?  Then make the
influence of sector a piece of evidence, and then you change your a-priori
question to "Given 100 companies, when I take the average across all
sectors, how many of them ...?"

#  (Notice how asking the a-priori question sometimes helps in knowledge
refinement by forcing hidden influences out into the open.  But, in this
case, you already have a 'Strong Sector' attribute.)

#  'Will grow' depends on sector, and that is already taken care of, via
the item 'Strong sector'.  But perhaps 'Vision for growth' also depends on
sector, in that in certain sectors the culture of that sector is for growth
while in others it is not.  The I.T. sector tends to have a growth culture
while the agricultural sector might not.  The construction industry at
present is still in recession.  The sports sector seems to have a culture
of growing.  So let us take these four as an example.  We will employ yet
another facility of Istar, the Chooser inference method.

#  What we want is a piece of evidence for 'Vision for growth' that depends
on sector.  Place a bayesian 'Sector vision' as antecedent to it.  Bring up
its attribute details panel and change its inference method to Chooser.
The Chooser takes an integer (or an Enum or Ordinal) as its first
antecedent, and then all the others should normally be the same type as the
consequent.  The Chooser first finds the value of the first antecedent.
Then, if 1, it takes the first of the rest (i.e. the second antecedent), if
2 it takes the 2nd of the rest, and so on, and copies its value into the
consequent.  In this case the attributes from which it chooses will be
sector constants.

#  Select Integer item type and place an integer attribute as antecedent to
'Sector vision'.  Label is 'Sector Id' with meaning "The identifier for the
sector".  Give it a question text of "To which sector does the company
belong?  1 = I.T., 2 = Agriculture, 3 = Construction, 4 = Sports".  (For a
less clumsy method than this long question, see Enumerated Types below.)

#  Now create four bayesian attributes as antecedents to 'Sector vision'
and, by bringing up their attribute panels, make each a Constant.  Then
give each a name and value something like:

      'I.T. vision'  90%
      'Agriculture vision'  10%
      'Construction vision' 20%
      'Sports vision' 60%

Try resetting and inferring 'Sector vision' to see what you obtain with
different values of the Id.

#  (If you wish, you could also change 'Strong sector' from being an input
information to a Chooser controlled by 'Sector Id' and to take its value
from a similar bank of constants.  Then, by answering 'Sector Id', the user
would automatically supply values to both 'Strong sector' and 'Sector
vision'.)

#  Now, don't forget to set the a-priori for 'Vision for growth' as an
average across all sectors.  If you expect about the same number of
companies in each sector then you could just take the average of the
sectors you are dealing with (90+10+20+60/4=45%).  The a-priori for 'Sector
vision' can stay at 50% since it is merely a modifying factor.

#  (Tip:  Don't worry too much about accuracy of a-prioris or weights; they
are more tolerant than you might think.)

#  We have dealt with a-prioris.  Now to deal with weights of evidence.  We
will take as an example the item 'Will grow' and its antecedents 'Strong
sector' and 'Vision for growth'.  In doing so we deal with odds rather than
probabilities.  Odds and probabilities are linked as:

      O = P / (1 - P)

and

      P = O / (1 + O).

In Istar odds are held as two integers, numerator and denominator.

#  Let us suppose you have set the a-priori for 'Will grow' to 25%.  Its
odds are therefore 0.25 / (1 - 0.25) = 0.25 / 0.75 = 1/3 ("one to three
against").

#  For each antecedent we ask two questions:

      QT:  "Suppose we know that the antecedent is completely true; then
      what would the belief in the consequent be?" and

      QF:  "Suppose we know that the antecedent is completely false; then
      what would the belief in the consequent be?"

Ask them of 'Strong sector':  "Suppose we know for certain that it is a
strong sector; what would our belief in 'Will grow' be?"  and conversely.
Suppose we find QT gives 75% (odds = 3/1) and QF gives 10% (odds = 1/9).

#  This gives us the means to work out the weights on the link between
them.  QT provides the left hand pair of numbers on the relationship
instance panel, and QF the right hand.  Bring up the panel.

#  Each pair is an odds multiplier, such that when multiplied into the a-
priori odds of the consequent gives the answer to QT and QF respectively.
Take the left hand pair and QT.

      A-priori odds of 'Will grow' = 1/3.
      Answer to QT = 3/1.
      Therefore odds multiplier must be 9/1.

So alter the left hand pair to 9/1.

#  In a similar way, the right hand pair and QF:

      A-priori odds of 'Will grow' = 1/3.
      Answer to QF = 1/9.
      Therefore odds multiplier must be 1/3 (which it already is).

Click on the 'OK' button.

#  Now process the others link weights in a similar manner:

      #  Convert the a-priori of the consequent to odds.
      #  Ask QT for the antecedent and consequent.
      #  Convert the answer to odds.
      #  Alter the left hand odds multiplier as appropriate.
      #  Ask GF and convert the answer to odds.
      #  Alter the right hand odds multiplier as appropriate.

#  Save the KB as 'R2'.

#  Now run the KB.  You should start to find the results are more
meaningful and interesting than before.  (NOTE:  At present there is a wee
bug in the Chooser, in not resetting its antecedents.  To overcome this,
you might have to press Reset a couple of times.)

#  To summarize, in the section we have:

      #  Concerned ourselves with KB accuracy.
      #  Met with a-priori probabilities for bayesians
      #  Found a simple question to ask to calculate them.
      #  Noted that this can unearth other factors.
      #  Used the Chooser.
      #  Set the weights of evidence ..
      #  .. by using odds and two simple questions.



14.  MAKING YOUR KBS MORE USABLE

In this section we note some ways to make the KB more usable, easier to
use, more friendly to the user.

#  Enumerators

The Chooser was driven by an integer, which meant the question text had to
be cumbersome.  A more serious problem was that the user was free to enter
any value.  Future versions of Istar might have value checking on input for
integers etc. but a better method is available now: use an Enumerated Type.

      An enumerated type is an identifier of which option is selected from
a set, e.g. which business sector.  One is already available as standard:
Weekdays.  But we must create another.

#  Load 'R2' if not already up.
#  On main control panel, click the Atts button.
#  (See Driving section on Creating New Attributes for details.)
#  Add a new attribute whose name is 'Sector' and whose type is Enumerated.
#  Remember to click the left Add button.
#  On the right hand side, enter in the 'N' gadget "I.T." and click Add.
#  Enter "Agriculture" and click Add.
#  Enter "Construction" and click Add.
#  Enter "Sports" and click Add.
#  Click Done.
#  Bring up the attribute details panel for 'Sector Id'.  Click the value
type wee button to bring up list of attribute types.  Select 'Sector'.
Click 'OK' on the type list.
#  Now click 'OK' on the attribute details panel.
#  Reset/infer 'Sector vision' and you should find it will no longer ask
you for an integer but give you a cycle gadget showing the sector names.
#  Now you can change the question text for 'Sector Id' to suit.

      It is often useful to create Enumerated types for the major
identifiers your KB uses.  Some will be Enums, some Ordinals.  Ordinals
differ from Enums in that the options are in some numeric order rather than
being just options.  e.g. Low, medium and high.


#  The order in which questions are asked

The order in which questions are asked is seldom important to the final
results given by a KB but can make a lot of difference to the user.  For
instance it is sensible to have the major questions at the start and also
to try to put questions relating to a given topic together.  The order in
which they are asked depends on the backward chaining process, the
inference methods employed and the answers to previous questions.  But
there are several ways of controlling the order of asking.

#  By altering the order of antecedents of an inference attribute.
#  By understanding the order imposed by certain inference methods.  For
instance, in Chooser the first antecedent must be completely asked and
answered before any of the others are sought, so all the questions that
pertain to the first will be asked first.  Similarly, in the comparison
methods (A > the rest, etc.) the first must be asked first.


#  Suppressing irrelevant questions

Often certain questions are irrelevant to the flow of the run of a KB.  For
instance for the I.T. sector it is irrelvant to ask about farming
practices.  There are various ways to prevent irrelevant questions being
asked.

#  The Chooser.  This will backward chain only up the chosen route and will
ignore the others.
#  Bayesian cut-offs.  Setting these will stop seeking input once it is
certain that the value of the bayesian attribute either cannot exceed the
lower cut-off or cannot be less than the upper cut-off, however the
remaining unanswered antecedents are answered.
#  Certain arithmetic and logical inference methods stop when an extreme
answer is known.  e.g. the process stops when: one of the antecedents is
zero in multiplication, when one is false in boolean AND, when one is true
in boolean OR.
#  In the comparisons that give a boolean result, as soon as the comparison
fails the process stops.  So "A > the rest" will stop as soon as one of the
rest is found to be >= A.


#  Forms

The normal method of obtaining information when running a KB in Istar is
question by question, since that allows the session to be most responsive
to the way information given.  But sometimes it is useful to have several
related pieces of information all on the same screen.

      For this, Istar offers a simple 'Forms' facility.

#  (Load a KB, such as R1.)
#  Select 'Form' item type.
#  Place an item of this type to the left of a number of attributes on your
KB.  Notice it has no name (though it can be given one).
#  Then, draw a link from the Form item to one of the attributes.
#  Then to another, and another.
#  Run the whole KB.  You should find that the three attributes you have
linked to the form are all presented on a single screen.
#  Answer them all, and click 'OK'.
#  Allow the rest of the questions to be asked.
#  Now click with right mouse button on the form item, and up comes an item
details panel.
#  Enter a name (label), some meaning.
#  Enter Form Text such as "Please give values for the following:".
#  Click 'OK'.
#  The label should now appear in the form box on the easel and the meaning
appear in the window when you move the mouse over it, just as with
attributes.
#  Run the KB and this time your Form Text should appear at the head of the
form.

      This facility is as yet undeveloped; new versions should appear
later.


#  Requiring less detail of the user

As it stands, our KB will ask us two things about the quality of
management.  As the KB develops we might find these split into several
more, which can feel rather too detailed to the user.  For this reason it
is sometimes useful to first ask a more general question about the overall
quality of management and derive these other factors from that.  Then, only
when detail is needed, ask these factors separately.

      There are several ways of doing this.  The easiest is to link all the
management factors back to 'Good management' as their single antecedent,
and give weights accordingly.

      But that does not differentiate much: they will all rise and fall
together.  There are several ways to overcome this, such as have another
item that asks whether the management's strengths are in marketing,
production or finance, and sets the various factors accordingly.




Copyright (c) Andrew Basden, 1996