Learning to Improve Uncertainty Handling in a Hybrid Planning System

Weaver is a hybrid planning algorithm that can create plans in domains that include uncertainty, modelled either as incomplete knowledge of the initial state of the world, of the effects of plan steps or of the possible external events. The plans are guaranteed to exceed some given threshold probability of success. Weaver creates a Bayesian network representation of a plan to evaluate it, in which links corresponding to sequences of events are computed with Markov models. As well as the probability of success, evaluation produces a set of flaws in the candidate plan, which are used by the planner to improve it. We describe a learning method that generates control knowledge compiled from this probabilistic evaluation of plans. The output of the learner is search control knowledge for the planning domain that helps the planner select alternatives that have previously lead to plans with high probability of success. The learned control knowledge is incrementally refined by a combined deductive and inductive mechanism.

[1]  S. Kambhampati,et al.  Learning Explanation-Based Search Control Rules for Partial Order Planning , 1994, AAAI.

[2]  John Mark Agosta,et al.  Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment , 1996, UAI.

[3]  Alfredo Milani,et al.  New directions in AI planning , 1996 .

[4]  Manuela M. Veloso,et al.  Incremental Learning of Control Knowledge for Nonlinear Problem Solving , 1994, ECML.

[5]  T. Dean,et al.  Planning under uncertainty: structural assumptions and computational leverage , 1996 .

[6]  Gregg Collins,et al.  Planning Under Uncertainty: Some Key Issues , 1995, IJCAI.

[7]  C. D. Gelatt,et al.  Lazy Explanation-based Learning: a Solu- Tion to the Intractable Theory Problem. in a Self- Organizing Retrieval System for Graphs. in 6. Conclusions and Ongoing Directions , 1991 .

[8]  Jaime G. Carbonell,et al.  Learning effective search control knowledge: an explanation-based approach , 1988 .

[9]  Oren Etzioni,et al.  DYNAMIC: A New Role for Training Problems in EBL , 1992, ML.

[10]  Tara A. Estlin,et al.  Multi-Strategy Learning of Search Control for Partial-Order Planning , 1996, AAAI/IAAI, Vol. 1.

[11]  Manuela M. Veloso,et al.  Learning strategy knowledge incrementally , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[12]  Oren Etzioni,et al.  Explanation-Based Learning: A Problem Solving Perspective , 1989, Artif. Intell..

[13]  William W. Cohen.  Learning Approximate Control Rules of High Utility , 1990, ML.

[14]  Manuela M. Veloso,et al.  Planning and Learning by Analogical Reasoning , 1994, Lecture Notes in Computer Science.

[15]  Ingrid Zukerman,et al.  Learning Search Control Rules for Planning: An Inductive Approach , 1991, ML.

[16]  Jim Blythe,et al.  Planning with External Events , 1994, UAI.

[17]  Tom Michael Mitchell,et al.  Explanation-based generalization: A unifying view , 1986 .

[18]  Eugene Fink,et al.  Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..

[19]  Nicholas Kushmerick,et al.  An Algorithm for Probabilistic Planning , 1995, Artif. Intell..