An optimal rewiring strategy for cooperative multiagent social learning

Multiagent coordination is a key problem in cooperative multiagent systems (MASs). It has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently neglected. First, the network topologies can change during the course of interaction dynamically. Second, the interaction utilities can be different among each pair of agents and usually unknown before interaction. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in a variety of large-scale MASs

Combination of self-organization mechanisms to enhance service discovery in open systems

Decentralized systems have emerged as an alternative to centralized approaches for dealing with dynamic requirements in new business models. These systems should provide mechanisms that contribute to flexibility and facilitate adaptation to changes in the environment. In this paper, we present two self-organization mechanisms for a decentralized service discovery system in order to improve its performance. These mechanisms are based on local actions of agents that only consider local information about queries they forward during the discovery process. The self-organization actions are chosen by each agent individually when the agent considers them to be appropriate. The actions are: remaining in the system, leaving the system, cloning, and changing structural relations with other agents. We have evaluated each self-organization mechanism separately but also the combination of the two as the environmental conditions in the service demand change. The results show that the proposed self-organization mechanisms considerably improve the performance of the service discovery systemDel Val Noguera, E.; Rebollo Pedruelo, M.; Botti Navarro, VJ. (2014). Combination of self-organization mechanisms to enhance service discovery in open systems. Information Sciences. 279:138-162. doi:10.1016/j.ins.2014.03.109S13816227

Establishing norms with metanorms over interaction topologies

Norms are a valuable means of establishing coherent cooperative behaviour in decentralised systems in which there is no central authority. Axelrod’s seminal model of norm establishment in populations of self-interested individuals provides some insight into the mechanisms needed to support this through the use of metanorms, but considers only limited scenarios and domains. While further developments of Axelrod’s model have addressed some of the limitations, there is still only limited consideration of such metanorm models with more realistic topological configurations. In response, this paper tries to address such limitation by considering its application to different topological structures. Our results suggest that norm establishment is achievable in lattices and small worlds, while such establishment is not achievable in scale-free networks, due to the problematic effects of hubs. The paper offers a solution, first by adjusting the model to more appropriately reflect the characteristics of the problem, and second by offering a new dynamic policy adaptation approach to learning the right behaviour. Experimental results demonstrate that this dynamic policy adaptation overcomes the difficulties posed by the asymmetric distribution of links in scale-free networks, leading to an absence of norm violation, and instead to norm emergence

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl