Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

Ming Chong Lim,Han-Lim Choi

doi:10.3390/app9102117

Ming Chong Lim, Han-Lim Choi

Open Access

https://doi.org/10.3390/app9102117

Copy DOI

Abstract

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.

Highlights

Application of multi-agent systems have often been considered for large-scale problems that are otherwise difficult or impossible to solve with only a single agent
We introduced a game-theoretic framework for allocation of tasks with spatial and temporal coupled constraints first seen in Constraint Consensus-Based Bundle Algorithm (CCBBA)
A game-theoretic modeling of such problems helps to overcome potential convergence issues faced in a market-based allocation model through leveraging on the properties of potential games

Summary

Introduction

Application of multi-agent systems have often been considered for large-scale problems that are otherwise difficult or impossible to solve with only a single agent. In traditional systems which are designed to achieve near-optimal solutions, agents are often programmed to be greedy and to seek maximum rewards for their actions leading to a competitive environment within the system, such that an agent may act in a manner that jeopardize the overall objective of the group for their own good. This concern leads to the adaptation of a game-theoretic framework which provide each individual agent freedom over its actions while at the same time, ensure that the solutions proposed by the collective action are socially acceptable and stable despite the players being self-regarding. Many game-theoretic models have been proposed to meet the requirements of different application scenarios and equilibrium selection algorithms ranging from constraint optimization [2,12] to learning [13,14,15] have been designed and shown to be feasible

Objectives

Results

Conclusion