Local Predictions for Case-Based Plan Recognition

Boris Kerkez and Michael T. Cox

Department of Computer Science and Engineering
Wright State University, Dayton, OH
{bkerkez,mcox}@cs.wright.edu



Abstract. This paper presents a novel case-based plan recognition system that
interprets observations of plan behavior using a case library of past
observations. The system is novel in that it represents a plan as a sequence of
action-state pairs rather than a sequence of actions preceded by some initial
state and followed by some final goal state. The system utilizes a unique
abstraction scheme to represent indices into the case base. The paper examines
and evaluates three different methods for prediction. The first method is
prediction without adaptation; the second is predication with adaptation, and the
third is prediction with heuristics. We show that the first method is better than a
baseline random prediction, that the second method is an improvement over the
first, and that the second and the third methods combined are the best overall
strategy.
References

Bares, M., Canamero, D., Delannoy, J. F., & Kodratoff, Y. (1994). XPlans: Case-based
reasoning for plan recognition. Applied Artificial Intelligence 8, 617-643.
Bergmann, R., & Wilke, W. (1995). Building and Refining Abstract Planning Cases by Change
of Representation Language. Journal of Artificial Intelligence Research, 3:53118.
Bergmann, R., & Wilke, W. (1996). On the Role of Abstractions in Case-Based Reasoning. In
EWCBR-96 European Conference on Case-Based Reasoning. Springer, 1996.
Carbonell, J. G., Blythe, J., Etzioni, O., Gil, Y., Joseph, R., Kahn, D., Knoblock, C., Minton, S.,
Perez, A., Reilly, S., Veloso, M., & Wang, X. (1992). PRODIGY 4.0: The Manual and
Tutorial (Tech. Rep. No. CMU-CS-92-150). Carnegie Mellon University, Department of
Computer Science, Pittsburgh, PA.
Cox, M. T., & Ram, A. (1999). Introspective multistrategy learning: On the construction of
learning strategies. Artificial Intelligence, 112, 1-55.
Kautz, H. (1991). A formal theory of plan recognition and its implementation. In J. Allen, et.
al., Reasoning about plans. San Francisco: Morgan Kaufmann.
Kerkez, B. (2001) Incremental case-based keyhole plan recognition. Technical Report, WSU-
CS-01-01, Department of Computer Science and Engineering, Wright State Univ.
Kerkez, B., & Cox, M. (2001). Case-based plan recognition using state indices, In D. W. Aha &
I. Watson (Eds.), Case-based Reasoning Research and Development: Proceedings of 4th
international conference on case-based reasoning (pp. 227-242). Berlin: Springer.
Kerkez, B. (2002). Learning Plan Libraries for Case-based Plan Recognition. In Proceedings of
the 13th Midwest Artificial Intelligence and Cognitive Science Conference. IIT, Chicago, IL.
Lesh, N., & Etzioni, O. (1996). Scaling up goal recognition. In Proceedings of the Fifth
Internat. Conference on Principles of Knowledge Representation and Reasoning (pp 178-
189).
Pazzani, M. (1994) Learning causal patterns: Making a transition from data-driven to theory-driven 
learning, in: R. Michalski and G. Tecuci, eds., Machine learning IV: A multistrategy
approach (Morgan Kaufmann, San Francisco, 1994) 267-293.
Veloso, M. (1994). Planning and learning by analogical reasoning. Berlin: Springer.
