Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

Abstract

This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a stronglyconnected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the solution, and additionally show that 50% of suboptimality can be at least guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation.

Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks

“…This utility function makes a dog less attracted by flocks with other dogs. By using the clustering algorithm [35], the dogs are divided into two teams autonomously, three of them herd the bigger flock and the remaining two dogs are assigned to the smaller flock after achieving Nash equilibrium. From t = 8 s, all the dogs are moving to the back of the flock and start pushing the flock to the goal location (represented by a red star).…”

A survey of task allocation techniques in MAS

“…As for the complexity, the Apollonius circle-based Active Pursuer Check (AAPC) [52], has complexity of Opn 2 a q, where n a is the number of pursuers. The complexity of GRAPE algorithm, based on an anonymous hedonic game [50], is bounded by Opd G n t n 2 a q, even though in most of the cases is much less, where d G is the graph diameter of the network, n t is the number of tasks and n a is the number of agents. As for the communication complexity of each agent it is Op|N i |n a q, where |N i | is the number of agents that the agent i communicates.…”

Multi-robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-threshold Approaches

“…The experiments demonstrate that the activation-threshold approach can distribute the load efficiently. In [25], they modeled the task allocation problem as a coalition-formation game (hedonic game) where self-interest agents form coalitions.…”