Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
An analysis of stochastic shortest path problems
Mathematics of Operations Research
Distinguishing tests for nondeterministic and probabilistic machines
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Automatic Recovery Using Bounded Partially Observable Markov Decision Processes
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Achieving goals in decentralized POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Piecewise linear dynamic programming for constrained POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Canadian traveler problem with remote sensing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
HTN-style planning in relational POMDPs using first-order FSCs
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
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For decision-theoretic planning problems with an indefinite horizon, plan execution terminates after a finite number of steps with probability one, but the number of steps until termination (i.e., the horizon) is uncertain and unbounded. In the traditional approach to modeling such problems, called a stochastic shortest-path problem, plan execution terminates when a particular state is reached, typically a goal state. We consider a model in which plan execution terminates when a stopping action is taken. We show that an action-based model of termination has several advantages for partially observable planning problems. It does not require a goal state to be fully observable; it does not require achievement of a goal state to be guaranteed; and it allows a proper policy to be found more easily. This framework allows many partially observable planning problems to be modeled in a more realistic way that does not require an artificial discount factor.