Abstract
Communication can guarantee the coordinated behavior in the multi-agent systems. However, in many real-world problems, communication may not be available at every time because of limited bandwidth, noisy environment or communication cost. In this paper, we introduce an algorithm to develop a communication strategy for cooperative multi-agent systems in which the communication is limited. This method employs a fuzzy model to estimate the benefit of communication for each possible situation. This specifies minimal communication that is necessary for successful joint behavior. An incremental method is also presented to create and tune our fuzzy model that reduces the high computational complexity of the multi-agent systems. We use several standard benchmark problems to assess the performance of our proposed method. Experimental results show that the generated communication strategy can improve the performance as well as full-communication strategy, while the agents utilize little communication.
Highlights
One of the main goals of artificial intelligence is designing autonomous agents interacting in a domain
We introduced an algorithm to develop a communication strategy for cooperative multi-agent systems in which the communication is limited
This strategy identifies best situations for making communication in Multi-Agent System (MAS) modelled by infinite-horizon Dec-Partially Observable Markov Decision Process (POMDP)
Summary
One of the main goals of artificial intelligence is designing autonomous agents interacting in a domain. Decentralized Partially Observable Markov Decision Process (Dec-POMDP) is a powerful framework for collaborative multi-agent planning in an uncertain environment [2]. In [12] an incremental fuzzy controller has been introduced to find a solution of large MASs. Our aim in this paper is to present an algorithm to identify best situations for making communication in MASs modelled by infinite-horizon Dec-POMDP. Our aim in this paper is to present an algorithm to identify best situations for making communication in MASs modelled by infinite-horizon Dec-POMDP This method develops a strategy that helps the agents to maintain coordination with minimal communication. This paper presents an incremental method to estimate the benefits of communication in every possible situation that the agents can have Based on this estimation, the agents can decide when the communication has the most impact on the improvement of the final performance. The belief space is an Ns-dimensional space defined by the belief vector
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