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.
Highlights
Compared with single-agent systems, multi-agent systems have the distribution properties of time, space, and function, and have several advantages in task applicability, execution efficiency, and system robustness [1]
While our work addresses a similar problem, we take a step further by formulating the language generation problem in the computational model of rationality
We provided a new coordination approach using simple languages for multi-agent systems
Summary
Compared with single-agent systems, multi-agent systems have the distribution properties of time, space, and function, and have several advantages in task applicability, execution efficiency, and system robustness [1]. A communication language or protocol should be predefined to inform cooperative strategies when designing multi-agent systems. Two methods are commonly used to construct languages for agent communication. One is to design a certain artificial language for agents [8,9]. The other is to let agents communicate in natural languages [10,11]. Most of the studies on multi-agent planning and distributed control use the former method to exchange messages. The latter approach is helpful for human partners to understand the behavior of agents. It is not necessary for agents to use human languages in a situation where only artificial agents exist.
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