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Planning under uncertainty has being increasingly demanded for practical applications in several areas that require reliable solutions for complex goals. In sight of this, in the last few years, some approaches based on formal methods for automatic synthesis of plans have been proposed in the area of Planning in Artificial Intelligence. Among these approaches, planning based on model checking seems to be a very attractive one; however, as we observe, the majority of the works in this approach are mainly based on CTL and deals only with planning problems for simple reachability goals (as those considered in classical planning). In this thesis, we introduce a more expressive class of planning goals (extended reachability goals) and show that, for this class of goals, the CTL’s semantics is not adequate to formalize algorithms for synthesis (or validation) of plans. As a way to overcome this limitation, we propose a new version of CTL, called α-CTL. Then, based on the semantics of this new logic, we implement a model checker (VACTL), based on which we also implement a planner (PACTL) capable of solving planning problems for extended reachability goals, in nondeterministic planning environments with complete observability. Finally, we discuss how to guarantee the quality of the solutions when we have an environment model where the actions transitions probabilities are known.

Planejamento sob incerteza para metas de alcançabilidade estendidas