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  • 1
    Online Resource
    Online Resource
    An Exponential Improvement on the MST Heuristic for Minimum Energy Broadcasting in Ad Hoc Wireless Networks
    Institute of Electrical and Electronics Engineers (IEEE) ; 2013
    In:  IEEE/ACM Transactions on Networking Vol. 21, No. 4 ( 2013-08), p. 1322-1331
    Details
    In: IEEE/ACM Transactions on Networking, Institute of Electrical and Electronics Engineers (IEEE), Vol. 21, No. 4 ( 2013-08), p. 1322-1331
    Type of Medium: Online Resource
    ISSN: 1063-6692 , 1558-2566
    URL: Article
    DOI: 10.1109/TNET.2012.2223483
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2013
    detail.hit.zdb_id: 2004853-1
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  • 2
    Online Resource
    Online Resource
    Unavailable Transit Feed Specification: Making It Available With Recurrent Neural Networks
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Transactions on Intelligent Transportation Systems Vol. 22, No. 4 ( 2021-4), p. 2111-2122
    Details
    In: IEEE Transactions on Intelligent Transportation Systems, Institute of Electrical and Electronics Engineers (IEEE), Vol. 22, No. 4 ( 2021-4), p. 2111-2122
    Type of Medium: Online Resource
    ISSN: 1524-9050 , 1558-0016
    URL: Article
    DOI: 10.1109/TITS.2021.3053373
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 2034300-0
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  • 3
    Online Resource
    Online Resource
    Strategyproof Mechanisms for Additively Separable and Fractional Hedonic Games
    AI Access Foundation ; 2021
    In:  Journal of Artificial Intelligence Research Vol. 70 ( 2021-03-26)
    Details
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 70 ( 2021-03-26)
    Abstract: Additively separable hedonic games and fractional hedonic games have received considerable attention in the literature. They are coalition formation games among selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions into coalitions) assuming that preferences are given. However, there is little discussion of this assumption. In fact, agents receive different utilities if they belong to different coalitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be run in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational complexity assumptions.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    URL: Article
    DOI: 10.1613/jair.1.12107
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2021
    detail.hit.zdb_id: 1468362-3
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  • 4
    Online Resource
    Online Resource
    On the Online Coalition Structure Generation Problem
    AI Access Foundation ; 2021
    In:  Journal of Artificial Intelligence Research Vol. 72 ( 2021-12-05), p. 1215-1250
    Details
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 72 ( 2021-12-05), p. 1215-1250
    Abstract: We consider the online version of the coalition structure generation problem, in which agents, corresponding to the vertices of a graph, appear in an online fashion and have to be partitioned into coalitions by an authority (i.e., an online algorithm). When an agent appears, the algorithm has to decide whether to put the agent into an existing coalition or to create a new one containing, at this moment, only her. The decision is irrevocable. The objective is partitioning agents into coalitions so as to maximize the resulting social welfare that is the sum of all coalition values. We consider two cases for the value of a coalition: (1) the sum of the weights of its edges, and (2) the sum of the weights of its edges divided by its size. Coalition structures appear in a variety of application in AI, multi-agent systems, networks, as well as in social networks, data analysis, computational biology, game theory, and scheduling. For each of the coalition value functions we consider the bounded and unbounded cases depending on whether or not the size of a coalition can exceed a given value α. Furthermore, we consider the case of a limited number of coalitions and various weight functions for the edges, i.e., unrestricted, positive and constant weights. We show tight or nearly tight bounds for the competitive ratio in each case.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    URL: Article
    DOI: 10.1613/jair.1.12989
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2021
    detail.hit.zdb_id: 1468362-3
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  • 5
    Online Resource
    Online Resource
    On Non-Cooperativeness in Social Distance Games
    AI Access Foundation ; 2019
    In:  Journal of Artificial Intelligence Research Vol. 66 ( 2019-11-11), p. 625-653
    Details
    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
    URL: Article
    DOI: 10.1613/jair.1.11808
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2019
    detail.hit.zdb_id: 1468362-3
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  • 6
    Online Resource
    Online Resource
    Nash Stable Outcomes in Fractional Hedonic Games: Existence, Efficiency and Computation
    AI Access Foundation ; 2018
    In:  Journal of Artificial Intelligence Research Vol. 62 ( 2018-06-21), p. 315-371
    Details
    In: Journal of Artificial Intelligence Research, AI Access Foundation, Vol. 62 ( 2018-06-21), p. 315-371
    Abstract: We consider fractional hedonic games, a subclass of coalition formation games that can be succinctly modeled by means of a graph in which nodes represent agents and edge weights the degree of preference of the corresponding endpoints. The happiness or utility of an agent for being in a coalition is the average value she ascribes to its members. We adopt Nash stable outcomes as the target solution concept; that is we focus on states in which no agent can improve her utility by unilaterally changing her own group. We provide existence, efficiency and complexity results for games played on both general and specific graph topologies. As to the efficiency results, we mainly study the quality of the best Nash stable outcome and refer to the ratio between the social welfare of an optimal coalition structure and the one of such an equilibrium as to the price of stability. In this respect, we remark that a best Nash stable outcome has a natural meaning of stability, since it is the optimal solution among the ones which can be accepted by selfish agents. We provide upper and lower bounds on the price of stability for different topologies, both in case of weighted and unweighted edges. Beside the results for general graphs, we give refined bounds for various specific cases, such as triangle-free, bipartite graphs and tree graphs. For these families, we also show how to efficiently compute Nash stable outcomes with provable good social welfare.
    Type of Medium: Online Resource
    ISSN: 1076-9757
    URL: Article
    DOI: 10.1613/jair.1.11211
    Language: Unknown
    Publisher: AI Access Foundation
    Publication Date: 2018
    detail.hit.zdb_id: 1468362-3
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  • 7
    Online Resource
    Online Resource
    Strategyproof Mechanisms for Friends and Enemies Games
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2020
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 02 ( 2020-04-03), p. 1950-1957
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 34, No. 02 ( 2020-04-03), p. 1950-1957
    Abstract: We investigate strategyproof mechanisms for Friends and Enemies Games, a subclass of Hedonic Games in which every agent classifies any other one as a friend or as an enemy. In this setting, we consider the two classical scenarios proposed in the literature, called Friends Appreciation (FA) and Enemies Aversion (EA). Roughly speaking, in the former each agent gives priority to the number of friends in her coalition, while in the latter to the number of enemies.We provide strategyproof mechanisms for both settings. More precisely, for FA we first present a deterministic n-approximation mechanism, and then show that a much better result can be accomplished by resorting to randomization. Namely, we provide a randomized mechanism whose expected approximation ratio is 4, and arbitrarily close to 4 with high probability. For EA, we give a simple (1+√2)n-approximation mechanism, and show that its performance is asymptotically tight by proving that it is NP-hard to approximate the optimal solution within O(n1−ɛ) for any fixed ɛ 〉 0.Finally, we show how to extend our results in the presence of neutrals, i.e., when agents can also be indifferent about other agents, and we discuss anonymity.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v34i02.5565
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2020
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  • 8
    Online Resource
    Online Resource
    Nash Stability in Social Distance Games
    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)
    Details
    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
    URL: Article
    DOI: 10.1609/aaai.v31i1.10608
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
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  • 9
    Online Resource
    Online Resource
    PAC Learning and Stabilizing Hedonic Games: Towards a Unifying Approach.
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2023
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37, No. 5 ( 2023-06-26), p. 5641-5648
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 37, No. 5 ( 2023-06-26), p. 5641-5648
    Abstract: We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. We first expand the known learnability/stabilizability landscape for some of the most prominent HGs classes, providing results for Friends and Enemies Games, Bottom Responsive, and Anonymous HGs. Then, having a broader view in mind, we attempt to shed light on the structural properties leading to learnability/stabilizability, or lack thereof, for specific HGs classes. Along this path, we focus on the fully expressive Hedonic Coalition Nets representation of HGs. We identify two sets of conditions that lead to efficient learnability, and which encompass all of the known positive learnability results. On the side of stability, we reveal that, while the freedom of choosing an ad hoc adversarial distribution is the most obvious hurdle to achieving PAC stability, it is not the only one. First, we show a distribution independent necessary condition for PAC stability. Then, we focus on W-games, where players have individual preferences over other players and evaluate coalitions based on the least preferred member. We prove that these games are PAC stabilizable under the class of bounded distributions, which assign positive probability mass to all coalitions. Finally, we discuss why such a result is not easily extendable to other HGs classes even in this promising scenario. Namely, we establish a purely computational property necessary for achieving PAC stability.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v37i5.25700
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2023
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  • 10
    Online Resource
    Online Resource
    On the Complexity of the Regenerator Placement Problem in Optical Networks
    Institute of Electrical and Electronics Engineers (IEEE) ; 2011
    In:  IEEE/ACM Transactions on Networking Vol. 19, No. 2 ( 2011-4), p. 498-511
    Details
    In: IEEE/ACM Transactions on Networking, Institute of Electrical and Electronics Engineers (IEEE), Vol. 19, No. 2 ( 2011-4), p. 498-511
    Type of Medium: Online Resource
    ISSN: 1063-6692 , 1558-2566
    URL: Journal
    URL: Article
    DOI: 10.1109/TNET.90
    DOI: 10.1109/TNET.2010.2068309
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2011
    detail.hit.zdb_id: 2004853-1
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