Heterogeneous Systems for Modeling Dynamic Worlds

The design of general problem solvers and general purpose representations has been a driving force in the field of artificial intelligence since its inception (see for example [26, 1, 24, 29]). While there are valid concerns about generality, an equally important issue is efficiency of inference. Special purpose representations are known to be good at that and have been used widely in Computer Science and Artificial Intelligence. We propose the use of heterogeneous systems for modeling dynamic worlds. A heterogeneous system is one that employs several kinds of representations. In particular, we are interested in systems that use sentential and diagrammatic languages in parallel. Diagrams are common place in communicating information and for problem solving (see newspapers, reports, and papers and books written on planning, qualitative reasoning, etc. [27, 8]. By combining special purpose diagrammatic representations with general purpose sentential representations we hope to get the best of both worlds, generality and efficiency.

[1]  K. Jon Barwise,et al.  Heterogeneous Reasoning , 1993, International Conference on Conceptual Structures.

[2]  H. Simon,et al.  Skill in Chess , 1988 .

[3]  R. Smullyan.  First-Order Logic , 1968 .

[4]  Allen Newell,et al.  GPS, a program that simulates human thought , 1995 .

[5]  Aaron Sloman,et al.  Interactions Between Philosophy and Artificial Intelligence: The Role of Intuition and Non-Logical Reasoning in Intelligence , 1971, IJCAI.

[6]  A. Tanenbaum.  Computer recreations , 1973 .

[7]  Patrick J. Hayes,et al.  Naive physics I: ontology for liquids , 1989 .

[8]  Daniel G. Bobrow,et al.  Qualitative Reasoning about Physical Systems: An Introduction , 1984, Artif. Intell..

[9]  James F. Allen.  Towards a General Theory of Action and Time , 1984, Artif. Intell..

[10]  N. Hari Narayanan,et al.  Towards a Theory of Commonsense Visual Reasoning , 1990, FSTTCS.

[11]  John R. Anderson,et al.  Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry , 1990, Cogn. Sci..

[12]  D. McDermott.  A Temporal Logic for Reasoning About Processes and Plans , 1982, Cogn. Sci..

[13]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987 .

[14]  Jon Barwise,et al.  Toward the rigorous use of diagrams in reasoning about hardware , 1996 .

[15]  Jeffrey Van Baalen.  Automated Design of Specialized Representations , 1992, Artif. Intell..

[16]  John McCarthy,et al.  Circumscription - A Form of Non-Monotonic Reasoning , 1980, Artif. Intell..

[17]  Keith Price,et al.  Review of "Principles of Artificial Intelligence by Nils J. Nilsson", Tioga Publishing Company, Palo Alto, CA, ISBN 0-935382-01-1. , 1980, SGAR.

[18]  David E. Wilkins,et al.  Domain-Independent Planning: Representation and Plan Generation , 1984, Artif. Intell..

[19]  Brian V. Funt,et al.  Problem-Solving with Diagrammatic Representations , 1980, Artif. Intell..

[20]  Saul Amarel,et al.  On representations of problems of reasoning about actions , 1968 .

[21]  A. Kox,et al.  THE COLLECTED PAPERS OF , 1996 .

[22]  J. M. Larrazabal,et al.  Reasoning about change , 1991 .

[23]  G. Plotkin,et al.  Situation Theory and its Applications , 1995 .

[24]  Dana S. Nau,et al.  On the Complexity of Blocks-World Planning , 1992, Artif. Intell..

[25]  Hubert L. Dreyfus,et al.  How to stop worrying about the frame problem even though it's computationally insoluble , 1987 .

[26]  Kurt Konolige,et al.  Reasoning with Analogical Representations , 1992, KR.

[27]  Eric Hammer,et al.  Reasoning with Sentences and Diagrams , 1994, Notre Dame J. Formal Log..

[28]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.