LTLf Best-Effort Synthesis in Nondeterministic Planning Domains

TL;DR

This paper introduces a game-theoretic method for synthesizing best-effort strategies in nondeterministic planning domains with goals in LTLf, improving scalability and effectiveness over previous approaches.

Contribution

It presents a novel game-theoretic synthesis technique for best-effort strategies in nondeterministic domains with LTLf goals, enhancing scalability and correctness.

Findings

Significantly improved scalability over previous methods

Formal proof of correctness for the synthesis technique

Experimental validation demonstrating effectiveness

Abstract

We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a game-theoretic technique for synthesizing best-effort strategies that exploit the specificity of nondeterministic planning domains. We formally show its correctness and demonstrate its effectiveness experimentally, exhibiting a much greater scalability with respect to a direct best-effort synthesis approach based on re-expressing the planning domain as generic environment specifications.