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From: Patricia.Mackiewicz@isl1.ri.cmu.EDU
Newsgroups: comp.ai.digest
Subject: Seminar - Reactive Learning (CMU)
Message-ID: <8704210656.AA12007@ucbvax.Berkeley.EDU>
Date: Fri, 17-Apr-87 14:59:21 EST
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Posted: Fri Apr 17 14:59:21 1987
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TOPIC:    Reactive Learning:  Experimentation and Decompilation 

SPEAKER:  Jaime Carbonell, CMU

WHEN:     Tuesday, April 21, 1987, 3:30 p.m.
WHERE:    Wean Hall 5409


Most symbolic learning approaches have been purely empirical (inductive)
or purely analytical.  The former extracts a general concept from a set of
empirical observations, whereas the latter composes primitive concepts
into larger units (chunks, macro-operators, "explanations", etc.).
Analytical methods include explanation-based learning, capable of
exploiting a complete domain theory to learn complex concepts from very few 
instances.  However, the domain theory may be partial, and judicious
integration of empirical and analytical methods may prove far superior
to either method alone.  Reactive experimentation is a case in point:
partial domain knowledge is used to formulate hypotheses, and empirical
data from the experiments is used to formulate new concepts or modify
existing ones.  Decompilation maps complex empirical observations into 
comprehensible operational units using analytical techniques.  Both
methods for combining analytical and empirical approaches are
explored with the objective of creating robust learning systems.