Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Principles of artificial intelligence
Principles of artificial intelligence
Formulation of tradeoffs in planning under uncertainty
Formulation of tradeoffs in planning under uncertainty
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Modular utility representation for decision-theoretic planning
Proceedings of the first international conference on Artificial intelligence planning systems
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
A model for projection and action
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Preferential semantics for goals
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
This paper describes a novel approach to planning which takes advantage of decision theory to greatly improve robustness in an uncertain environment. We present an algorithm which computes conditional plans of maximum expected utility. This algorithm relies on a representation of the search space as an AND/OR tree and employs a depth-limit to control computation costs. A numeric robustness factor, which parameterizes the utility function, allows the user to modulate the degree of risk-aversion employed by the planner. Via a look-ahead search, the planning algorithm seeks to find an optimal plan using expected utility as its optimization criterion. We present experimental results obtained by applying our algorithm to a non-deterministic extension of the blocks world domain. Our results demonstrate that the robustness factor governs the degree of risk embodied in the conditional plans computed by our algorithm.