Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Performance bounds for planning in unknown terrain
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Concise finite-domain representations for PDDL planning tasks
Artificial Intelligence
Probabilistic planning with clear preferences on missing information
Artificial Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Continual planning and acting in dynamic multiagent environments
Autonomous Agents and Multi-Agent Systems
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Replanning in domains with partial information and sensing actions
Journal of Artificial Intelligence Research
Integrated task and motion planning in belief space
International Journal of Robotics Research
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Improved integer programming approaches for chance-constrained stochastic programming
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Automating the evaluation of planning systems
AI Communications
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Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are invariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.