Looking for Pattern Matching and Learning method

Matt Sullivan (matt.sullivan@usa.net)
Wed, 21 Apr 1999 11:24:14 +0200 (MET DST)

Hello all,

I am looking for a pattern matching and learning algorithm that will allow
me to do the following. I have a list of concepts (known or learned by the
system), specified by a person, that attempts to describe some domain that
is formally specified within the system. Each concept can have a list of
attributes associated with it. The problem is, how to take the list of
concepts and attempt to match them to the domain that is most applicable
within the system, and to learn from mistakes made.

For example, say a person entered the following concepts (the syntax, made
up just to describe this problem, is <concept, attributes>): <'alloy
wheels', yes>, <'engine': 2 litre>, <'color', red>, <'milage', 10,000>. The
system has a large collection of domains, including one for a car, which
internally has a list of concepts such as make, model, colur, engine, etc.
There is also a description of motorbike, with much the same concepts.
However, the concepts within these domains may have constraints, so , for
example, the engine concept in motorbike would indicate that a 2 litre
engine is unlikely, whereas it is fine for a car. Note that I am not
attempting to match a user description to a particular car, just to the car
domain.

Can anyone point me to any algorithms or methods that could perform these
sorts of tasks?

Thanks,

Matt Sullivan

matt@europe.com

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