Foundations of Knowledge Acquisition pp 117-143 | Cite as
The Role of Self-Models in Learning to Plan
Chapter
Abstract
We argue that in order to learn to plan effectively, an agent needs an explicit model of its own planning and plan execution processes. Given such a model, the agent can pinpoint the elements of these processes that are responsible for an observed failure to perform as expected, which in turn enables the formulation of a repair designed to ensure that similar failures do not occur in the future. We have constructed simple models of a number of important components of an intentional agent, including threat detection, execution scheduling, and projection, and applied them to learning within the context of competitive games such as chess and checkers.
Keywords
Fault Diagnosis Priority Queue Plan Execution Intentional Agent General Problem Solver
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- Agre, P., and Chapman, D. (1987). Pengi: An implementation of a theory of activity. Proceedings of the 1987 AAAI Conference, Seattle, WA, pp. 268–272.Google Scholar
- Birnbaum, L., and Collins, G. (1988). The transfer of experience across planning domains through the acquisition of abstract strategies. Proceedings of the 1988 Workshop on Case-Based Reasoning, Clearwater Beach, FL, pp. 61–79.Google Scholar
- Birnbaum, L., Collins, G., Freed, M., and Krulwich, B. (1990). Model-based diagnosis of planning failures. Proceedings of the 1990 AAAI Conference, Boston, MA, pp. 318–323.Google Scholar
- Birnbaum, L., Collins, G., Brand, M., Freed, M., Krulwich, B., and Pryor, L. (1991). A model-based approach to the construction of adaptive case-based planning systems. Proceedings of the 1991 Workshop on Case-Based Reasoning, Washington, DC, pp. 215–224.Google Scholar
- Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, vol. 2,no. 1.Google Scholar
- Carbonell, J. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In R. Michalski, J. Carbonell, and T. Mitchell, eds., Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, Los Altos, CA, pp. 371–392.Google Scholar
- Collins, G., Birnbaum, L., and Krulwich, B. (1989). An adaptive model of decision-making in planning. Proceedings of the Eleventh IJCAI, Detroit, MI, pp. 511–516.Google Scholar
- Collins, G., Birnbaum, L., Krulwich, B., and Freed, M. (1991). Plan debugging in an intentional system. Proceedings of the Twelfth IJCAI, Sydney, Australia, pp. 353–358.Google Scholar
- Davis, R. (1984). Diagnostic reasoning based on structure and behavior. Artificial Intelligence, vol. 24, pp. 347–410.CrossRefGoogle Scholar
- DeJong, G., and Mooney, R. 1986. Explanation-based learning: An alternative view. Machine Learning, vol. 1, pp. 145–176.Google Scholar
- deKleer, J., and Williams, B. (1987). Diagnosing multiple faults. Artificial Intelligence, vol. 32, pp. 97–130.CrossRefGoogle Scholar
- Doyle, J. (1979). A truth maintenance system. Artificial Intelligence, vol. 12, pp. 231–272.CrossRefMathSciNetGoogle Scholar
- Fikes, R., and Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, vol. 2, pp. 189–208.MATHCrossRefGoogle Scholar
- Firby, R. (1989). Adaptive execution in complex dynamic worlds. Research report no. 672, Yale University, Dept. of Computer Science, New Haven, CT.Google Scholar
- Hammond, K. (1989a). Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, San Diego.Google Scholar
- Hammond, K. (1989b). Opportunistic memory. Proceedings of the Eleventh IJCAI, Detroit, MI, pp. 504–510.Google Scholar
- Hayes-Roth, F. (1983). Using proofs and refutations to learn from experience. In R. Michalski, J. Carbonell, and T. Mitchell, eds., Machine Learning: An Artificial Intelligence Approach, Vol. 1, Tioga, Palo Alto, CA, pp. 221–240.Google Scholar
- Kolodner, J. (1987). Capitalizing on failure through case-based inference. Proceedings of the Ninth Cognitive Science Conference, Seattle, WA, pp. 715–726.Google Scholar
- Krulwich, B. (1991). Determining what to learn in a multi-component planning system. Proceedings of the Thirteenth Cognitive Science Conference, Chicago, IL, pp. 102–107.Google Scholar
- Mitchell, T., Keller, R., and Kedar-Cabelli, S. (1986). Explanation-based generalization: A unifying view. Machine Learning, vol. 1, pp. 47–80.Google Scholar
- Newell, A., and Simon, H. (1963). GPS, a program that simulates human thought. In E. Feigenbaum and J. Feldman, eds., Computers and Thought, McGraw-Hill, New York, pp. 279–293.Google Scholar
- Sacerdoti, E. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence, vol. 5, pp. 115–132.MATHCrossRefGoogle Scholar
- Sacerdoti, E. (1977). A Structure for Plans and Behavior. American Elsevier, New York.MATHGoogle Scholar
- Schank, R. (1982). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press, Cambridge, England.Google Scholar
- Schank, R., Collins, G., and Hunter, L. (1986). Transcending inductive category formation in learning. The Behavioral and Brain Sciences, vol. 9, pp. 639–686.CrossRefGoogle Scholar
- Simmons, R. (1988). A theory of debugging plans and interpretations. Proceedings of the 1988 AAAI Conference, St. Paul, MN, pp. 94–99.Google Scholar
- Stallman, R., and Sussman, G. (1977). Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, vol. 9, pp. 135–196.MATHCrossRefGoogle Scholar
- Sussman, G. (1975). A Computer Model of Skill Acquisition. American Elsevier, New York.Google Scholar
- Wilkins, D. (1984). Domain independent planning: Representation and plan generation. Artificial Intelligence, vol. 22, pp. 269–302.CrossRefGoogle Scholar
Copyright information
© Kluwer Academic Publishers 1993