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Introduction
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A Planner for Efficient
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Conclusion
Contents
Future Work
Subsections
Introduction
Evaluation
Comparing reinforcement learning with planning
Dialogues with human beings
Improvements to design features of the planner
Evaluation of game trees using a logical model of belief
Beliefs about plan rules
Improved belief revision
Multilogue planning
Dealing with complexity
Further example problems
Long-range planning and user-model acquisition
Dry-land algorithm
Knowledge engineering and large problems
Negotiation dialogue
Request and propose
Planning in self-interested settings
Focussing and plan recognition in negotiation dialogue
Combining statistical methods in
speech recognition with
probabilistic planning
Conclusion
bmceleney 2006-12-19
