The complexity of Markov decision processes
Mathematics of Operations Research
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Applications of SHOP and SHOP2
IEEE Intelligent Systems
Indefinite-horizon POMDPs with action-based termination
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A decision-theoretic approach to task assistance for persons with dementia
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Advanced user assistance based on AI planning
Cognitive Systems Research
A companion technology for cognitive technical systems
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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In this paper, a novel approach to hierarchical planning under partial observability in relational domains is presented. It combines hierarchical task network planning with the finite state controller (FSC) policy representation for partially observable Markov decision processes. Based on a new first-order generalization of FSCs, action hierarchies are defined as in traditional hierarchical planning, so that planning corresponds to finding the best plan in a given decomposition hierarchy of predefined, partially abstract FSCs. Finally, we propose an algorithm for solving planning problems in this setting. Our approach offers a way of practically dealing with real-world partial observability planning problems: it avoids the complexity originating fromthe dynamic programming backup operation required in many present-day policy generation algorithms.