We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to efficient and fair allocations based on a simple convention of courtesy. We prove that following such convention induces a strategy which constitutes an @math -subgame-perfect equilibrium of the repeated allocation game with discounting. Simulation results highlight the effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms, since it achieves more than two orders of magnitude faster convergence, higher efficiency, fairness, and average payoff.
Acknowledgements. Foreword by W.V. Quine. Introduction. I. Coordination and Convention. Sample Coordination Problems. Analysis of Coordination Problems. Solving Coordination Problems. Convention. Sample Conventions. II. Convention Refined. Common Knowledge. Knowledge of Conventions. Alternatives to Convention. Degrees of Convention. Consequences of Conventions. III. Convention Contrasted. Agreement. Social Contracts. Norms. Rules. Conformative Behavior. Imitation. Meaning of Signals. IV. Convention and Communication. Sample Signals. Analysis of Signaling. Verbal Signaling. Conventional Meaning of Signals. V. Conventions of Language. Possible Languages. Grammars. Semantics in a Possible Language. Conventions of Truthfulness. Semantics in a Population. Conclusion. Index. The field of convention emergence studies how agents involved in repeated coordination games can reach consensus through only local interactions. The literature on this topic is vast and is motivated by human societies, mainly addressing coordination problems between human agents, such as who gets to redial after a dropped telephone call. In contrast, real-world engineering problems, such as coordination in wireless sensor networks, involve agents with limited resources and knowledge and thus pose certain restrictions on the complexity of the coordination mechanisms. Due to these restrictions, strategies proposed for human coordination may not be suitable for engineering applications and need to be further explored in the context of real-world application domains. In this article we take the role of designers of large decentralized multi-agent systems. We investigate the factors that speed up the convergence process of agents arranged in different static and dynamic topologies and under different interaction models, typical for engineering applications. We also study coordination problems both under partial observability and in the presence of faults (or noise). The main contributions of this article are that we propose an approach for emergent coordination, motivated by highly constrained devices, such as wireless nodes and swarm bots, in the absence of a central entity and perform extensive theoretical and empirical studies. Our approach is called Win-Stay Lose-probabilistic-Shift, generalizing two well-known strategies in game theory that have been applied in other domains. We demonstrate that our approach performs well in different settings under limited information and imposes minimal system requirements, due to its simplicity. Moreover, our technique outperforms state-of-the-art coordination mechanisms, guarantees full convergence in any topology and has the property that all convention states are absorbing.  The purpose of conventions is to coordinate people's expectations in economic and social interactions that have multiple equilibria. Conventions often emerge endogenously from the accumulation of many precedents, a process that can be modeled as a stochastic dynamical system. The theory leads to specific predictions about the ways in which conventions form and are displaced, and identifies key properties of conventions that are most likely to withstand the test of time. The theory is illustrated by the evolution of left-wing driving conventions in Europe and patterns of sharecropping contracts in agriculture. In this paper, we investigate techniques via which a group of autonomous agents can reach a global agreement on the use of social conventions by using only locally available information. Such conventions play a central role in naturallyoccurring social systems, and there are good masons tbr supposing that they will play a similarly important role in artificial social systems. Following a short review of conventions and their use in distributed artificial intelligence, we present a formal model that rigorously defines both our experimental methodology, and the performance measures we use [o quantify the success of our experiments. We then describe sixteen different mechanisms for bringing about agreement on conventions, and present experimental results obtained for each of these methods. A tentative analysis of these results is given, and the paper concludes with some comments and issues for future work.