Contingent planning under uncertainty via stochastic satisfiability
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Conditional, probabilistic planning: a unifying algorithm and effective search control mechanisms
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Probabilistic Planning in the Graphplan Framework
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Decision-theoretic planning
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Heuristic search + symbolic model checking = efficient conformant planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Simulation-based planning for planetary rover experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
Collaboration among a satellite swarm
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Planning for human-robot teaming in open worlds
ACM Transactions on Intelligent Systems and Technology (TIST)
Fault-tolerant planning under uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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For a given problem, the optimal Markov policy over a finite horizon is a conditional plan containing a potentially large number of branches. However, there are applications where it is desirable to strictly limit the number of decision points and branches in a plan. This raises the question of how one goes about finding optimal plans containing only a limited number of branches. In this paper, we present an any-time algorithm for optimal k-contingency planning. It is the first optimal algorithm for limited contingency planning that is not an explicit enumeration of possible contingent plans. By modelling the problem as a partially observable Markov decision process, it implements the Bellman optimality principle and prunes the solution space. We present experimental results of applying this algorithm to some simple test cases.