Paper
GenePlan: Evolving Better Generalized PDDL Plans using Large Language Models
Authors
Andrew Murray, Danial Dervovic, Alberto Pozanco, Michael Cashmore
Abstract
We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate domain-dependent generalized planners for classical planning tasks described in PDDL. By casting generalized planning as an optimization problem, GenePlan iteratively evolves interpretable Python planners that minimize plan length across diverse problem instances. In empirical evaluation across six existing benchmark domains and two new domains, GenePlan achieved an average SAT score of 0.91, closely matching the performance of the state-of-the-art planners (SAT score 0.93), and significantly outperforming other LLM-based baselines such as chain-of-thought (CoT) prompting (average SAT score 0.64). The generated planners solve new instances rapidly (average 0.49 seconds per task) and at low cost (average $1.82 per domain using GPT-4o).
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.09481v1</id>\n <title>GenePlan: Evolving Better Generalized PDDL Plans using Large Language Models</title>\n <updated>2026-03-10T10:32:05Z</updated>\n <link href='https://arxiv.org/abs/2603.09481v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.09481v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate domain-dependent generalized planners for classical planning tasks described in PDDL. By casting generalized planning as an optimization problem, GenePlan iteratively evolves interpretable Python planners that minimize plan length across diverse problem instances. In empirical evaluation across six existing benchmark domains and two new domains, GenePlan achieved an average SAT score of 0.91, closely matching the performance of the state-of-the-art planners (SAT score 0.93), and significantly outperforming other LLM-based baselines such as chain-of-thought (CoT) prompting (average SAT score 0.64). The generated planners solve new instances rapidly (average 0.49 seconds per task) and at low cost (average $1.82 per domain using GPT-4o).</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-10T10:32:05Z</published>\n <arxiv:comment>54 pages, 4 figures. Accepted to ICAPS 2026</arxiv:comment>\n <arxiv:primary_category term='cs.AI'/>\n <author>\n <name>Andrew Murray</name>\n </author>\n <author>\n <name>Danial Dervovic</name>\n </author>\n <author>\n <name>Alberto Pozanco</name>\n </author>\n <author>\n <name>Michael Cashmore</name>\n </author>\n </entry>"
}