Strong Cyclic Planning with Incomplete Information and
Sensing
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Abstract Incomplete information and sensing are needed
in order to design agents that operate in domains
where their information acquisition capabilities
are restricted and the environment may evolve in
unpredictable ways. These representation issues,
that have been studied by the work on reasoning
about actions, are also being addressed from a
planning perspective. The aim of this paper is
to present a language KL for expressing planning
domains with incomplete knowledge and sensing
and by providing a new technique for generating
cyclic plans in such a framework. The basic
feature of the representation is the notion of
belief state which is characterized in terms of
epistemic formulas, representing the knowledge
of the agent about the environment. The proposed
planning method deals with such belief
states using propositional reasoning as a building
block.
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