Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Reasoning about plans
Using abstractions for decision-theoretic planning with time constraints
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
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
The *-minimax search procedure for trees containing chance nodes
Artificial Intelligence
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Maintaining consistency in a robot's knowledge-base via diagnostic reasoning
AI Communications - Intelligent Engineering Techniques for Knowledge Bases
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We investigate planning in time-critical domains represented as Markov Decision Processes, showing that search based techniques can be a very powerful method for finding close to optimal plans. To reduce the computational cost of planning in these domains, we execute actions as we construct the plan, and sacrifice optimality by searching to a fixed depth and using a heuristic function to estimate the value of states. Although this paper concentrates on the search algorithm, we also discuss ways of constructing heuristic functions suitable for this approach. Our results show that by interleaving search and execution, close to optimal policies can be found without the computational requirements of other approaches.