Knowledge Representation

Way back in the early days of computing, folks were fascinated by
the possibility that computers might be able to exhibit intelligent
behavior if given the right kinds of knowledge.  Problems like
solving puzzles and playing games were thought to be great domains
of study in the world of artificial intelligence, as only an entity
as intelligent as a human could solve a tile puzzle or play 
checkers.  As time went on, artificial intelligence researchers
realized that solving puzzles and playing games could be mastered
with relatively little intelligence, while more mundane tasks such
as reading a newspaper article or frying an egg without setting
the house on fire required much more intelligence than they
previously thought necessary.  Along the way, however, AI folks
have devoted a lot of thought to how to represent the various
kinds of knowledge required to get a computer to do something
that at least looks like intelligent behavior.  The ideas that
have resulted from those efforts, while by no means conclusive,
are now a part of mainstream computer science; you may never
do any AI work once you get out of Georgia Tech, but you may
find these ideas worth knowing, regardless of where your personal
voyage through computing takes you.
 
As we venture into the world of game playing, we're now faced
with questions about what kinds of knowledge are necessary to
play the game and how we're going to represent that knowledge.
As noted above, these are the same kinds of questions that AI
folks have been wrestling with for years.  Resorting to broad
sweeping generalizations, we can use five questions to drive the
analysis of a complex problem, most of which involve knowledge:
 
1)  What exactly is the activity that you want from the system
    you're going to create to solve this complex problem?
 
2)  What does your system need to know in order to perform
    that activity?
 
3)  How are you going to encode or represent that knowledge
    inside your system?  (e.g., What will the language of 
    symbols be?  What will the symbols map to? etc.)
 
4)  How will the system know which piece(s) of knowledge to use
    at a given time, and how will the system get at the appropriate
    knowledge without looking at all the knowledge?
 
5)  Once the system finds the appropriate knowledge, how will it
    use the knowledge?
 
You can think of item 1 as "defining the task or function of the
system", items 2 and 3 as "defining the knowledge representation
of the system", and items 4 and 5 as "defining the process of the
system":
 
  Task:  what the system does (i.e., what's the goal of the system?)
  Knowledge:  what the system needs to know to perform the task
  Process:  how the system performs the task
 
 
Good news, bad news
 
The good news (according to AI guru Elaine Rich, from whom most of
this knowledge representation stuff is borrowed) is that once a
problem is described using an appropriate representation, the 
problem is almost solved.  The bad news, however, is that describing
the knowledge correctly is pretty darn hard.  Why?  First, the
knowledge to perform some complex task well is usually fairly huge.
Second, that knowledge is probably pretty difficult to characterize
accurately.  Third, if the system using that knowledge is going to
be flexible, that knowledge is probably not going to remain 
constant; it'll probably change constantly.  
 
If we were to try to draw inspiration from how humans represent
knowledge, we might first think of how humans traditionally
represent knowledge externally, for posterity.  A quick look
at the library tells us that humans use languages common to 
their respective cultures as well as pictures.  But while those
representations might be useful for transferring knowledge 
between individuals or between generations (within limits...a lot
of languages have died over the past few thousand years), they're
probably not what humans use to represent large amounts of knowledge
inside their heads.  For example, while there is undoubtedly some
text in your head, it would be easy to demonstrate that you don't
record everything you hear or read as if you were a tape recorder.
Similarly, while there's certainly imagery in your head, the images
are by no means exact duplicates of what you saw...just ask any
criminal lawyer who depends on eyewitness testimony to make or
break a case.
 
Text and pictures aren't very good representations for computers,
either.  Why?  They tend to be open to interpretation:  they're
ambiguous (the same expression can mean many things), they're
vague (sometimes it's hard to find any meaning), and the 
"vocabulary" of representation tends to be huge and open-ended.
So what makes a good knowledge representation scheme for a computer?
 
 
The specifics of knowledge representation
 
Before we get into the attributes of a good knowledge representation
scheme, let's get a little bit more specific about what a knowledge
representation scheme actually is.  A knowledge representation 
scheme is a set of conventions about how to describe a class of 
things.  A description makes use of the conventions of a 
representation to describe some particular thing within that class
of things.  A given representation needs a set of symbols (i.e.,
a vocabulary) with some understood mapping between the symbols
and primitives in the "world" being represented (e.g., objects,
attributes, relationships, etc.).  The representation also needs
some rules or conventions for how to order or combine symbols into
more complex expressions which then become descriptions; these
rules can be thought of as a syntax or grammar for the 
representation language.  Finally, a representation scheme needs
a set of operators or procedures which permit the creation and
manipulation of descriptions.  Got it?  Good.
 
 
What makes a good knowledge representation scheme?
 
There are a number of desirable characteristics of a knowledge 
scheme.  It's never possible to maximize all the attributes; 
some will be sacrificed for others.  But it's always good to know
what the trade-offs are so that you can make better informed 
decisions.
 
1.  The representation should capture generalities that exist in the
world that's being modelled.
 
2.  The representation should be easily modifiable to reflect
changes so that new knowledge can be derived from old knowledge
(AI folks call this "inference") or so that entirely new 
information can be added (this is "acquisition" or, sometimes,
"learning").
 
3.  It should be understandable by the people who provide the
knowledge as well as those who may have to look at it later (so 
that they don't have to convert the knowledge to or from some weird 
form they don't understand).  This attribute is also known as
"transparency".
 
4.  It should be usable even if it is not entirely accurate or
complete.
 
5.  The important objects and relationships should be explicitly
represented.
 
6.  Any natural constraints on how one object or relation influences
another should be obvious.
 
7.  Irrelevant detail should be suppressed (but it would be nice if
you could get at the details when necessary).  Does the word
"abstraction" ring a bell?
 
8.  The representation should be complete---you should be able to
represent everything that needs to be represented.
 
9.  It should be concise, in that you can say what needs to be said
efficiently.
 
10. It should be fast, in that you can store and retrieve 
information quickly.
 
 
So now you're knowledge representation experts.  Well, no, not 
really.  But you know all the right kinds of questions to ask, and
you know how to tell if some possible answers to those questions are
better than others.  And that makes you smarter, so what more could
you ask for?

Last updated on Thu Aug 6 15:21:03 EDT 1998 by Brian McNamara