Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark

TL;DR

This paper introduces a tunable benchmark suite for multi-objective AI planning and evaluates variants of the DAE-YAHSP evolutionary planner, highlighting its potential for advancing multi-objective planning research.

Contribution

It proposes a new benchmark suite for multi-objective planning and assesses the performance of DAE-YAHSP variants on this benchmark.

Findings

Multi-objective DAE-YAHSP performs well on the benchmark

Benchmark suite enables testing of multi-objective planning algorithms

Results support future multi-objective planning competitions

Abstract

All standard AI planners to-date can only handle a single objective, and the only way for them to take into account multiple objectives is by aggregation of the objectives. Furthermore, and in deep contrast with the single objective case, there exists no benchmark problems on which to test the algorithms for multi-objective planning. Divide and Evolve (DAE) is an evolutionary planner that won the (single-objective) deterministic temporal satisficing track in the last International Planning Competition. Even though it uses intensively the classical (and hence single-objective) planner YAHSP, it is possible to turn DAE-YAHSP into a multi-objective evolutionary planner. A tunable benchmark suite for multi-objective planning is first proposed, and the performances of several variants of multi-objective DAE-YAHSP are compared on different instances of this benchmark, hopefully paving the road to further multi-objective competitions in AI planning.