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Newsgroups: ont.events
From: marina@ai.toronto.edu (Marina Haloulos)
Subject: Dr. Michael R. Lowry, Thursday 23 November 1989: ARTIFICIAL INTELLIGEN
Message-ID: <89Nov13.144700est.2836@neat.cs.toronto.edu>
Date: 13 Nov 89 19:47:29 GMT

           Department of Computer Science, University of Toronto
             (GB = Gailbraith Building, 35 St. George Street)

       -------------------------------------------------------------

                      ARTIFICIAL INTELLIGENCE SEMINAR
              GB119, at 11:00 a.m., Thursday 23 November 1989

                           Dr. Michael R. Lowry
                             Kestrel Institute

                "Problem Reformulation through Abstraction,
                       Design, then Implementation"

 The effectiveness of intelligent problem-solving systems is highly
 dependent on their representation of knowledge and formulation
 of problems. An intelligent system which automatically
 reformulates problems and changes representation is more
 effective at problem-solving than current AI systems which work
 within a fixed representation.

 This talk will present a theory of problem reformulation and a
 prototype reformulation system, STRATA, whose domain is
 algorithm synthesis. STRATA synthesizes algorithms in three
 steps. First it abstracts a problem by discovering problem
 properties and incorporating them into the domain theory.
  Second, it designs an abstract algorithm to solve the abstracted
 problem. Third, STRATA constructs an efficient implementation
 for the abstract algorithm. The end result of these three steps is a
 change of representation.

 This talk will emphasize an algebraic theory of problem
 abstraction. Abstraction is defined semantically on the problem
 domain rather than syntactically on the representation of the
 problem domain.  This algebraic framework stresses both the
 abstraction of objects and the abstraction of operations in a
 problem domain.  Problem abstraction is a specialized type of
 abstraction in which essential information is preserved while
 inessential information is thrown away.

 The semantic definition of problem abstraction is independent of
 representation system. This talk will describe how semantic
 abstraction can be realized as syntactic transformations of logical
 theories. A formal logic which is sound with respect to the
 semantics of abstraction has been developed. A method for
 calculating within this logic called Ontological Reasoning has been
 implemented. STRATA employs ontological reasoning to generate
 problem reformulations which represent problem abstractions.

 The mathematical foundation for this theory of problem
 reformulation is based on recent work in theoretical computer

 science, particularly abstract data types. This will be briefly
 described in the talk and is detailed in the thesis.