Punishment in Multiagent Systems
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Maintaining control over the autonomous agents is a major concern in MultiAgent Systems (MAS). Social laws or norms are used to specify the expected ideal behaviors from the agents. Although several norm enforcement mechanisms are developed to encourage the agents to remain compliant with the norms or the social laws, still there is a lack of a formal analysis of punishment in MAS. In this thesis, we develop punishment models and analyze certain implementation issues. Our contributions are twofold, firstly, we model the punishment procedure and then, we study certain side effects of executing the punishment. We model a MAS as a network and punishment as cuts in that network. A cut separates the violators from the compliant agents. As they can not interact with the compliant agents, they are deprived from the utility that they would get from executing certain joint actions with the compliant agents. Hence they get punished. This form of punishment is common in our society such as `jail' or `economic sanctions'. In this context, we use auctions, coalitional games and party affiliation game to analyze the punishment procedure. Based on these models of punishment we develop a punishment regimentation mechanism, that compels the compliant agents to punish the violators. Additionally, we use NAE-SAT games to analyze the adverse eects of such regimentation. In the second part of the thesis, we study the side effects of isolating the violators, which can decrease the connectivity of the MAS. As connectivity is decreased, agents are less likely to collaborate. Hence the efficiency of the MAS also decreases. We use edge augmentation to recover connectivity. In this context, we study Nash equilibrium of an edge augmentation game and nally, we use multiple source spanning tree completion problems to study more complex scenarios of connectivity recovery.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Item Access Status
Multi agent systems