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  • 1
    Online Resource
    Online Resource
    AI Access Foundation ; 2019
    In:  Journal of Artificial Intelligence Research Vol. 66 ( 2019-11-11), p. 625-653
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 66 ( 2019-11-11), p. 625-653
    Abstract: We consider Social Distance Games (SDGs), that is cluster formation games in which the utility of each agent only depends on the composition of the cluster she belongs to, proportionally to her harmonic centrality, i.e., to the average inverse distance from the other agents in the cluster. Under a non-cooperative perspective, we adopt Nash stable outcomes, in which no agent can improve her utility by unilaterally changing her coalition, as the target solution concept. Although a Nash equilibrium for a SDG can always be computed in polynomial time, we obtain a negative result concerning the game convergence and we prove that computing a Nash equilibrium that maximizes the social welfare is NP-hard by a polynomial time reduction from the NP-complete Restricted Exact Cover by 3-Sets problem. We then focus on the performance of Nash equilibria and provide matching upper bound and lower bounds on the price of anarchy of Θ(n), where n is the number of nodes of the underlying graph. Moreover, we show that there exists a class of SDGs having a lower bound on the price of stability of 6/5 − ε, for any ε 〉 0. Finally, we characterize the price of stability 5 of SDGs for graphs with girth 4 and girth at least 5, the girth being the length of the shortest cycle in the graph.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2019
    detail.hit.zdb_id: 1468362-3
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  • 2
    Online Resource
    Online Resource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1 ( 2017-02-10)
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 31, No. 1 ( 2017-02-10)
    Abstract: We consider Social Distance Games (SDGs), that is cluster formation games in which agent utilities are proportional to their harmonic centralities in the respective coalitions, i.e., to the average inverse distance from the other agents. We adopt Nash stable outcomes, that is states in which no agent can improve her utility by unilaterally changing her coalition, as the target solution concept. Although SDGs always admit a Nash equilibrium, we prove that it is NP-hard to find a social welfare maximizing one and obtain a negative result concerning the game convergence. We then focus on the performance of Nash equilibria and provide matching upper bound and lower bounds on the price of anarchy of Θ(n), where n is the number of nodes of the underlying graph, and a lower bound on the price of stability of 6/5 - ε. Finally, we characterize the price of stability of SDGs for graphs with girth 4 and girth at least 5.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
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  • 3
    Online Resource
    Online Resource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1 ( 2017-02-10)
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 31, No. 1 ( 2017-02-10)
    Abstract: We investigate Pareto stability in Social Distance Games, that are coalition forming games in which agents utilities are proportional to their harmonic centralities in the respective coalitions, i.e., to the average inverse distance from the other agents. Pareto optimal solutions have been already considered in the literature as outcomes arising from the strategic interaction of the agents. In particular, they are stable under the deviation of the grand coalition, as they do not permit a simultaneous deviation by all the agents making all of them weakly better off and some strictly better off. We first show that, while computing a Pareto stable solution maximizing the social welfare is NP-hard in bounded degree graphs, a 2 min{Delta,sqrt n}-approximating one can be determined in polynomial time, where n is the number of agents and Delta the maximum node degree. We then determine asymptotically tight bounds on the Price of Pareto Optimality for several classes of social graphs arising from the following combinations: unbounded and bounded node degree, undirected and directed edges, unweighted and weighted edges.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
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  • 4
    Online Resource
    Online Resource
    Elsevier BV ; 2022
    In:  Artificial Intelligence Vol. 312 ( 2022-11), p. 103768-
    In: Artificial Intelligence, Elsevier BV, Vol. 312 ( 2022-11), p. 103768-
    Type of Medium: Online Resource
    ISSN: 0004-3702
    RVK:
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1468341-6
    detail.hit.zdb_id: 218797-8
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