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arXiv · 2204.04322

Iterative Depth-First Search for Fully Observable Non-Deterministic Planning

Ramon Fraga Pereira·André G. Pereira·Frederico Messa·Giuseppe De Giacomo
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Abstract

Fully Observable Non-Deterministic (FOND) planning models uncertainty through actions with non-deterministic effects. Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process. We compare our proposed algorithm to well-known FOND planners, and show that it has robust performance over several distinct types of FOND domains considering different metrics.

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Ramon Fraga Pereira, André G. Pereira, Frederico Messa, Giuseppe De Giacomo. 2022-06-20. Iterative Depth-First Search for Fully Observable Non-Deterministic Planning. https://arxiv.org/abs/2204.04322

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