The QBKG system : knowledge representation for producing and explaining judgments

The QBKG system plays backgammon and produces critical analyses of possible moves for a wide variety of backgammon positions, using a hierarchically structured, non-discrete form of knowledge representation. The largely non-searching control structure emphasizes judgemental processes at the expense of reasoning processes, meaning that the system's behavior is determined by the estimated usefulness of its immediate actions rather than upon hypothesized longer-term results such as would be produced by a tree-searching algorithm. This report describes some of the principles by which knowledge can be represented so as to facilitate high-quality judgements in a domain, discusses issues arising from the need to be able to explain how a particular judgement was reached, and argues that sophisticated judgemental ability is a critical feature for systems operating in complex, incompletely understood environments. ^This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory Under Contract F33615-81-K-1539. D. H. Ackley & H. J. Berliner i Table of

[1]  Hans J. Berliner,et al.  Some Necessary Conditions for a Master Chess Program , 1973, IJCAI.

[2]  Hans J. Berliner,et al.  Backgammon Computer Program Beats World Champion , 1980 .

[3]  Douglas B. Lenat.  Learning program helps win national fleet wargame tournament , 1982, SGAR.

[4]  Terry Winograd,et al.  Understanding natural language , 1974 .

[5]  Jon Doyle,et al.  A selected descriptor-indexed bibliography to the literature on belief revision , 1980, SGAR.

[6]  Victor Lesser,et al.  Organization of the Hearsay II speech understanding system , 1975 .

[7]  Edward H. Shortliffe,et al.  Production Rules as a Representation for a Knowledge-Based Consultation Program , 1977, Artif. Intell..

[8]  Douglas B. Lenat,et al.  The Nature of Heuristics , 1982, Artif. Intell..

[9]  Hans J. Berliner,et al.  On the Construction of Evaluation Functions for Large Domains , 1979, IJCAI.

[10]  J. F. Baldwin,et al.  Fuzzy Switching and Automata Theory and Applications , 1980 .

[11]  David L. Waltz,et al.  Understanding Line drawings of Scenes with Shadows , 1975 .

[12]  E. Robinson.  Cybernetics, or Control and Communication in the Animal and the Machine , 1963 .

[13]  Paul S. Rosenbloom,et al.  A World-Championship-Level Othello Program , 1982, Artif. Intell..

[14]  Geoffrey E. Hinton,et al.  OPTIMAL PERCEPTUAL INFERENCE , 1983 .

[15]  David H. Ackley,et al.  The QBKG System: Generating Explanations From a Non-Discrete Knowledge Representation , 1982, AAAI.

[16]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[17]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[18]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[19]  Douglas B. Lenat,et al.  Heuristic Search for New Microcircuit Structures: An Application of Artificial Intelligence , 1982, AI Mag..

[20]  Allen Newell,et al.  Physical Symbol Systems , 1980, Cogn. Sci..