Work

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

Year: 2022

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2204.04322

Authors: Pereira, Ramon Fraga, Pereira, André G., Messa, Frederico, De Giacomo, Giuseppe

Institutions: Sapienza University of Rome, Universidade Federal do Rio Grande do Sul

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Universidade Federal do Rio Grande do Sul

Awards: G1207375514, G1620329843, G1920939207, G2554800379, G2818464215

OpenAlex ID: W4223572054

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Cited by: 1

Related to: 10