Automatic OBDD-based generation of universal plans in non-deterministic domains
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Strong Cyclic Planning Revisited
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Planning with a language for extended goals
Eighteenth national conference on Artificial intelligence
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Strong planning under partial observability
Artificial Intelligence
Action representation and partially observable planning using epistemic logic
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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 as model checking for extended goals in non-deterministic domains
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Structured plans and observation reduction for plans with contexts
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Efficient abstraction and refinement for behavioral description based web service composition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Behavioural description based web service composition using abstraction and refinement
International Journal of Web and Grid Services
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Strong planning under full or partial observability has been addressed in the literature. But this research line is carried out under the hypothesis that the set of observation variables is fixed and compulsory. In most real world domains, however, observation variables are optional and many of them are useless in the execution of a plan; on the other side, information acquisition may require some kind of cost. So it is significant to find a minimal set of observation variables which are necessary for the execution of a plan, and to best of our knowledge, it is still an open problem. In this paper we present a first attempt to solve the problem, namely, we define an algorithmthat finds an approximateminimal set of observation variables which are necessary for the execution of a strong plan under full observability (i.e. a state-action table); and transforms the plan into a strong plan under partial observability (i.e. a conditional plan branching on the observations built on these observation variables).