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Publication - Professor Weiru Liu

    Branching-Bounded Contingent Planning via Belief Space Search

    Citation

    McAreavey, K, Bauters, K, Liu, W & Hong, J, 2019, ‘Branching-Bounded Contingent Planning via Belief Space Search’. in: 2nd ICAPS Workshop on Explainable AI Planning (XAIP'19).

    Abstract

    A contingent plan can be encoded as a rooted graph where branching occurs due to sensing. In many applications it is desirable to limit this branching; either to reduce the complexity of the plan (e.g. for subsequent execution by a human), or because sensing itself is deemed to be too expensive. This leads to an established planning problem that we refer to as branching-bounded contingent planning. In this paper, we formalise solutions to such problems in the context of history-, and belief-based policies: under noisy sensing, these policies exhibit differing notions of sensor actions. We also propose a new algorithm, called BAO*, that is able to find optimal solutions via belief space search. This work subsumes both conformant and contingent planning frameworks, and represents the first practical treatment of branching-bounded contingent planning that is valid under partial observability.

    Full details in the University publications repository