Abstract
Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference.
Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it.
This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience.
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References
C. Rieger, “Conceptual memory and inference,” in R.C. Schank, editor, Conceptual Information Processing. North-Holland: Amsterdam, 1975.
A. Ram and D. Leake, “Evaluation of explanatory hypotheses,” in Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, Chicago, IL, August 1991.
T.G. Dietterich, “Limitations on inductive learning,” in Proceedings of Sixth International Workshop on Machine Learning, pages 125–128, Ithaca, NY, June 1989. Morgan Kaufman.
P. Utgoff, “Shift of bias for inductive concept learning,” in R.S. Michalshi, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, Vol. II, pages 107–148. Morgan Kaufman: Los Altos, CA, 1986.
S. Minton, Learning effective search control knowledge: An explanation-based approach, Ph.D. thesis, Carnegie-Mellon University, Computer Science Department, Pittsburgh, PA, 1988. Technical Report CMU-CS-88-133.
H. Zukier, “The paradigmatic and narrative modes in goal-guided inference,” in R. Sorrentino and E. Higgins, editors, Handbook of Motivation and Cognition: Foundations of Social Behavior, pages 465–502. Guilford Press: Guilford, CT, 1986.
C. Hoffman, W. Mischel, and K. Mazze, “The role of purpose in the organization of information about behavior: Trait-based versus goal-based categories in person cognition,” Journal of Personality and Social Psychology, 39:211–255, 1981.
T. Srull and R. Wyer, “The role of chronic and temporary goals in social information processing,” in R. Sorrentino and E. Higgins, editors, Handbook of Motivation and Cognition: Foundations of Social Behavior, pages 503–549. The Guilford Press, Guilford, CT, 1986.
G.F. DeJong, Skimming Stories in Real Time: An Experiment in Integrated Understanding, Ph.D. thesis, Yale University, Department of Computer Science, New Haven, CT, May 1979. Research Report #158.
M.G. Dyer, In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension, Ph.D. thesis, Yale University, Department of Computer Science, New Haven, CT, May 1982. Research Report #116.
A. Ram, “AQUA: Asking questions and understanding answers,” in Proceedings of the Sixth Annual National Conference on Artificial Intelligence, pages 312–316. Seattle, WA, July 1987. Morgan Kaufman Publishers, Inc.
A. Ram, Question-driven understanding: An integrated theory of story understanding, memory and learning. Ph.D. thesis, Yale University, New Haven, CT, May 1989. Research Report #710.
L.E. Hunter, Knowledge acquisition planning: Gaining expertise through experience, Ph.D. thesis, Yale University, Department of Computer Science, New Haven, CT, January 1989. Research Report #678.
A. Ram, “A theory of questions and question asking,” The Journal of the Learning Sciences, 1(3/4) 273–318, 1991.
A. Ram, “Decision models: A theory of volitional explanation,” in Proceedings of the Twelvth Annual Conference of the Cognitive Science Society, pages 198–205, Cambridge, MA, July 1990. Lawrence Erlbaum Associates.
R.C. Schank, Explanation Patterns: Understanding Mechanically and Creatively, Lawrence Erlbaum Associates: Hillsdale, NJ, 1986.
A. Kass, D. Leake, and C. Owens, SWALE: A Program That Explains, in R.C. Shank, editor, Explanation Paterns: Understanding Mechanically and Creatively, pages 232–254. Lawrence Erlbaum Associates: Hillsdale, NJ, 1986.
L.E. Hunter, “Planning to learn,” in Proceedings of the Twelvth Annual Conference of the Cognitive Science Society, pp. 26–34, Boston, MA, July 1990.
L.E. Hunter, “Knowledge acquisition planning for inference from large datasets,” In Proceedings of the Twenty Third Annual Hawaii International Conference on System Sciences, pages 35–44, Kona, HI, 1990. IEEE Press.
R. Yesner and D. Carter, “Pathology of carcinoma of the lung: Changing patterns,” Clinics in Chest Medicine, 3(2):257–289, 1982.
R.C. Schank and R. Abelson, Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Lawrence Erlbaum Associates: Hillsdale, NJ, 1977.
A. Ram, “Knowledge goals: A theory of interestingness,” in Proceedings of the Twelvth Annual Conference of the Cognitive Science Society, pages 206–214, Cambridge, MA, July 1990. Lawrence Erlbaum Associates.
R. Wilensky, “Knowledge representation—A critique and a proposal,” in J.L. Kolodner and C.K. Riesbeck, editors, Experience, Memory and Reasoning, chapter 2, pages 15–28. Lawrence Erlbaum Associates: Hillsdale, NJ, 1986.
S. Hidi and W. Baird, “Interestingness—A neglected variable in discourse processing,” Cognitive Science, 10:179–194, 1986.
R.C. Schank, “Interestingness: Controlling inferences,” Artificial Intelligence, 12:273–297, 1979.
F. Hayes-Roth and V. Lesser, “Focus of attention in a distributed logic speech understanding system,” in Proceedings of the IEEE International Conference on ASSP, Philadelphia, PA, 1976.
D. Sperber and D. Wilson, Relevance: Communication and Cognition, Language and Thought Series. Harvard University Press, Cambridge, MA, 1986.
A. Ram, “Incremental learning of explanation patterns and their indices,” in Proceedings of the Seventh International Conference on Machine Learning, pages 313–320, Austin, TX, June 1990. Morgan Kaufmann Publishers, Inc.
R.C. Schank, Dynamic Memory: A Theory of Learning in Computers and People, Cambridge University Press, 1982.
P. Livesey, Learning and emotion: A biological synthesis, volume 1 of Evolutionary Processes. Lawrence Erlbaum Associates, Hillsdale, NJ, 1986.
D. B. Lenat, A.M.: An artificial intelligence approach to discovery in mathematics as heuristic search, Ph.D. thesis, Stanford University, Artificial Intelligence Laboratory, 1976.
C. Tong, “Towards an engineering science of knowledge-based design,” AI/VLSI Project Working Paper 49, Rutgers University, Department of Computer Science, New Brunswick, NJ, 1987.
G.J. Sussman, A Computer Model of Skill Acquisition, volume 1 of Artificial Intelligence Series. American Elsevier: New York, 1975.
E.D. Sacerdoti, “A structure for plans and behavior,” Technical Report 109, Stanford Research Institute, Artificial Intelligence Center, 1975.
M.J. Stefik, “Planning with constraints (MOLGEN: Part 1),” Artificial Intelligence, 16(2): 111–140, 1981.
B. Hayes-Roth and F. Hayes-Roth, “A cognitive model of planning,” Cognitive Science, 2: 275–310, 1979.
K.J. Hammond, “Opportunistic memory: Storing and recalling suspended goals,” in J.L. Kolodner, editor, Proceedings of a Workshop on Case-Based Reasoning, pages 154–168, Clearwater Beach, FL, May 1988, Morgan Kaufmann, Inc.
L. Birnbaum and G. Collins, “Opportunistic planning and Freudian slips,” in Proceedings of the Sixth Annual Conference of the Cognitive Science Society, Boulder, CO, 1984.
N. Dehn, Computer Story Writing: The Role of Reconstructive and Dynamic Memory, Ph. D. thesis, Yale University, Department of Computer Science, New Haven, CT, 1989.
E. Horvitz, G. Cooper, and D. Heckerman, “Reflection and action under scarce resources: Theoretical principles and empirical study,” Report KSL-89-1, Knowledge Systems Laboratory, Stanford University, 1989.
A. Luria, The Mind of a Mnemonist, New York, 1968.
D. Dennett, The Intentional Stance, Bradford Books/MIT Press: Boston, MA, 1987.
J. Doyle, “A truth maintenance system,” Artificial Intelligence, 12: 231–272, 1979.
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Ram, A., Hunter, L. The use of explicit goals for knowledge to guide inference and learning. Appl Intell 2, 47–73 (1992). https://doi.org/10.1007/BF00058575
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DOI: https://doi.org/10.1007/BF00058575