Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Mobile Robotics Planning Using Abstract Markov Decision Processes
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Qualitative MDPs and POMDPs: an order-of-magnitude approximation
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Structured reachability analysis for Markov decision processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Fast belief update using order-of-magnitude probabilities
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Efficient decision-theoretic planning: techniques and empirical analysis
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Correlated action effects in decision theoretic regression
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Fast value iteration for goal-directed Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Plan development using local probabilistic models
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Sound abstraction of probabilistic actions in the constraint mass assignment framework
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Theoretical foundations for abstraction-based probabilistic planning
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
A structured, probabilistic representation of action
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Integrating planning and execution in stochastic domains
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
A probabilistic model of action for least-commitment planning with information gathering
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Deliberation scheduling for time-critical sequential decision making
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Adaptation and decision-making driven by emotional memories
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Generating admissible heuristics by abstraction for search in stochastic domains
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Heuristic search when time matters
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
We provide a method, based on the theory of Markov decision problems, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards. Standard goals of achievement, as well as goals of maintenance and prioritized combinations of goals, can be specified in this way. An optimal policy can be found using existing methods, but these methods are at best polynomial in the number of states in the domain, where the number of states is exponential in the number of propositions (or state variables). By using information about the starting state, the reward function, and the transition probabilities of the domain, we can restrict the planner's attention to a set of world states that are likely to be encountered in satisfying the goal. Furthermore, the planner can generate more or less complete plans depending on the time it has available. We describe experiments involving a mobile robotics application and consider the problem of schedulilng different phases of the planning algorithm given time constraints.