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Anticipation is a general concept used and applied in various domains. Many studies in the field of artificial intelligence have investigated the capacity for anticipation. In this article, we focus on the use of anticipation in multi-agent coordination, particularly preventive anticipation which consists of anticipating undesirable future situations in order to avoid them. We propose to use constraint processing to formalize preventive anticipation in the context of multi-agent coordination. The resulting algorithm allows any action that may induce an undesirable future state to be detected upstream of any multi-agent coordination process. Our proposed method is instantiated in a road traffic simulation tool. For the specific question of simulating traffic at road junctions, our results show that taking anticipation into account allows globally realistic behaviors to be reproduced without provoking gridlock between the simulated vehicles.

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One of the crucial issues in multi-agent systems is the coordination of agents' actions. A simple way to coordinate the agents is to define norms in the system. Most of the research related to norms and multi-agent systems focuses on designing normative agents to ensure a social order inside the system. To achieve this goal, these studies make the assumption that nonnormative behavior must be controlled using sanction mechanisms. This paper discusses the relevance of this assumption in certain specific contexts, and presents an alternative for controlling non-normative behavior, thus ensuring the social order. To illustrate our proposal, we introduce a concrete application involving traffic simulation at a road junction. Based on the statistical traffic results, we show how our approach helps to improve the realism of the simulation.