Planning in nondeterministic domains with temporally extended goals under partial observability is one of the most challenging problems in planning. Simpler subsets of this problem have been already addressed in the literature, but the general combination of extended goals and partial observability is, to the best of our knowledge, still an open problem. In this paper we present a first attempt to solve the problem, namely, we define an algorithm that builds plans in the general setting of planning with extended goals and partial observability. The algorithm builds on the top of the techniques developed in the planning with model checking framework for the restricted problems of extended goals and of partial observabilit
Planning with partial observability can be formulated as a non-deterministic search problem in belie...
Planning with sensing actions under partial observability is a computationally challenging problem t...
We present algorithms for partially observable planning that iteratively compute belief states with ...
Planning in nondeterministic domains with temporally ex-tended goals under partial observability is ...
Planning in nondeterministic domains with temporally extended goals under partial observability is ...
Planning in nondeterministic domains with temporally ex-tended goals under partial observability is ...
Planning in nondeterministic domains with temporally extended goals under partial observability is o...
The increasing interest in planning in nondeterministic domains by model checking has seen the recen...
Recent research has addressed the problem of planning in non-deterministic domains. Classical planni...
Methods that interleave planning and execution are a practical solution to deal with complex plannin...
AbstractRarely planning domains are fully observable. For this reason, the ability to deal with part...
Rarely planning domains are fully observable. For this reason, the ability to deal with partial obse...
Abstract. Methods that interleave planning and execution are a practical solution to deal with compl...
Several real world applications require planners that deal with non-deterministic domains and with t...
Planning with partial observability can be formulated as a non-deterministic search problem in belie...
Planning with partial observability can be formulated as a non-deterministic search problem in belie...
Planning with sensing actions under partial observability is a computationally challenging problem t...
We present algorithms for partially observable planning that iteratively compute belief states with ...
Planning in nondeterministic domains with temporally ex-tended goals under partial observability is ...
Planning in nondeterministic domains with temporally extended goals under partial observability is ...
Planning in nondeterministic domains with temporally ex-tended goals under partial observability is ...
Planning in nondeterministic domains with temporally extended goals under partial observability is o...
The increasing interest in planning in nondeterministic domains by model checking has seen the recen...
Recent research has addressed the problem of planning in non-deterministic domains. Classical planni...
Methods that interleave planning and execution are a practical solution to deal with complex plannin...
AbstractRarely planning domains are fully observable. For this reason, the ability to deal with part...
Rarely planning domains are fully observable. For this reason, the ability to deal with partial obse...
Abstract. Methods that interleave planning and execution are a practical solution to deal with compl...
Several real world applications require planners that deal with non-deterministic domains and with t...
Planning with partial observability can be formulated as a non-deterministic search problem in belie...
Planning with partial observability can be formulated as a non-deterministic search problem in belie...
Planning with sensing actions under partial observability is a computationally challenging problem t...
We present algorithms for partially observable planning that iteratively compute belief states with ...