Coordination of Multiple Autonomous Agents Using Naturally Generated Languages in Task Planning

Abstract

Language plays a prominent role in the activities of human beings and other intelligent creatures. One of the most important functions of languages is communication. Inspired by this, we attempt to develop a novel language for cooperation between artificial agents. The language generation problem has been studied earlier in the context of evolutionary games in computational linguistics. In this paper, we take a different approach by formulating it in the computational model of rationality in a multi-agent planning setting. This paper includes three main parts: First, we present a language generation problem that is connected to state abstraction and introduce a few of the languages’ properties. Second, we give the sufficient and necessary conditions of a valid abstraction with proofs and develop an efficient algorithm to construct the languages where several words are generated naturally. The sentences composed of words can be used by agents to regulate their behaviors during task planning. Finally, we conduct several experiments to evaluate the benefits of the languages in a variety of scenarios of a path-planning domain. The empirical results demonstrate that our languages lead to reduction in communication cost and behavior restriction.