Autonomous Task Planning for Heterogeneous Multi-Agent Systems

TL;DR

This paper introduces a formal framework for optimal and heuristic task planning in heterogeneous multi-agent systems using nondeterministic finite automata, accommodating constraints, capabilities, and failure modes.

Contribution

It develops a novel, formal automata-based approach for multi-agent task planning that guarantees optimality and completeness, with a scalable heuristic variant for efficiency.

Findings

The framework guarantees optimal and complete solutions.

A heuristic method reduces computational complexity significantly.

Case studies validate the effectiveness of the proposed approach.

Abstract

This paper presents a solution to the automatic task planning problem for multi-agent systems. A formal framework is developed based on the Nondeterministic Finite Automata with $\epsilon$-transitions, where given the capabilities, constraints and failure modes of the agents involved, an initial state of the system and a task specification, an optimal solution is generated that satisfies the system constraints and the task specification. The resulting solution is guaranteed to be complete and optimal; moreover a heuristic solution that offers significant reduction of the computational requirements while relaxing the completeness and optimality requirements is proposed. The constructed system model is independent from the initial condition and the task specification, alleviating the need to repeat the costly pre-processing cycle for solving other scenarios, while allowing the incorporation of failure modes on-the-fly. Two case studies are provided: a simple one to showcase the concepts of the proposed methodology and a more elaborate one to demonstrate the effectiveness and validity of the methodology.