Extension of MC-net-based coalition structure generation: handling negative rules and externalities
نویسندگان
چکیده
Forming effective coalitions is a major research challenge in AI and multi-agent systems. A Coalition Structure Generation (CSG) problem involves partitioning a set of agents into coalitions so that the social surplus is maximized. Ohta et al. introduce an innovative direction for solving CSG, i.e., by representing a characteristic function as a set of rules, a CSG problem can be formalized as the problem of finding a subset of rules that maximizes the sum of rule values under certain constraints. This paper considers two significant extensions of the formalization/algorithm of Ohta et al., i.e., (i) handling negative value rules and (ii) handling externalities among coalitions.
منابع مشابه
Handling negative value rules in MC-net-based coalition structure generation
A Coalition Structure Generation (CSG) problem involves partitioning a set of agents into coalitions so that the social surplus is maximized. Recently, Ohta et al. developed an efficient algorithm for solving CSG assuming that a characteristic function is represented by a set of rules, such as marginal contribution networks (MC-nets). In this paper, we extend the formalization of CSG in Ohta et...
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