Planning with Dynamically Estimated Action Costs

• URL: http://arxiv.org/abs/2206.04166v3
• Date: Wed, 19 Jul 2023 12:19:51 GMT
• Title: Planning with Dynamically Estimated Action Costs
• Authors: Eyal Weiss and Gal A. Kaminka
• Abstract summary: Information about action costs is critical for real-world AI planning applications. Recent approaches use black-box external action cost estimators, often learned from data, that are applied during the planning phase. We suggest a generalization of deterministic planning with action costs that allows selecting between multiple estimators for action cost.
• Score: 2.8326418377665346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information about action costs is critical for real-world AI planning applications. Rather than rely solely on declarative action models, recent approaches also use black-box external action cost estimators, often learned from data, that are applied during the planning phase. These, however, can be computationally expensive, and produce uncertain values. In this paper we suggest a generalization of deterministic planning with action costs that allows selecting between multiple estimators for action cost, to balance computation time against bounded estimation uncertainty. This enables a much richer -- and correspondingly more realistic -- problem representation. Importantly, it allows planners to bound plan accuracy, thereby increasing reliability, while reducing unnecessary computational burden, which is critical for scaling to large problems. We introduce a search algorithm, generalizing $A^*$, that solves such planning problems, and additional algorithmic extensions. In addition to theoretical guarantees, extensive experiments show considerable savings in runtime compared to alternatives.

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