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1. Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search

   https://doi.org/10.24963/ijcai.2024/27  

   Taguelmimt, Redha; Aknine, Samir; Boukredera, Djamila; Changder, Narayan; Sandholm, Tuomas (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   Coalition formation is a key capability in multi-agent systems. An important problem in coalition formation is coalition structure generation: partitioning agents into coalitions to optimize the social welfare. This is a challenging problem that has been the subject of active research for the past three decades. In this paper, we present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques. Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms. These algorithms use offline phases to optimize the choice of coalitions to evaluate. The third one uses branch-and-bound and integer partition graph search to explore the solution space. Our techniques bring a new way of approaching the problem and a new level of precision to the field. In experiments over several common value distributions, we show that the hybridization of these techniques in SMART is faster than the fastest prior algorithms (ODP-IP, BOSS) in generating optimal solutions across all the value distributions. 

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2. Two for One & One for All: Two-Sided Manipulation in Matching Markets

   https://doi.org/10.24963/ijcai.2022/46  

   Hosseini, Hadi; Umar, Fatima; Vaish, Rohit (July 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Main Track.)

   Strategic behavior in two-sided matching markets has been traditionally studied in a one-sided manipulation setting where the agent who misreports is also the intended beneficiary. Our work investigates two-sided manipulation of the deferred acceptance algorithm where the misreporting agent and the manipulator (or beneficiary) are on different sides. Specifically, we generalize the recently proposed accomplice manipulation model (where a man misreports on behalf of a woman) along two complementary dimensions: (a) the two for one model, with a pair of misreporting agents (man and woman) and a single beneficiary (the misreporting woman), and (b) the one for all model, with one misreporting agent (man) and a coalition of beneficiaries (all women). Our main contribution is to develop polynomial-time algorithms for finding an optimal manipulation in both settings. We obtain these results despite the fact that an optimal one for all strategy fails to be inconspicuous, while it is unclear whether an optimal two for one strategy satisfies the inconspicuousness property. We also study the conditions under which stability of the resulting matching is preserved. Experimentally, we show that two-sided manipulations are more frequently available and offer better quality matches than their one-sided counterparts. 

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3. Tiered Coalition Formation Games

   Siler, Cory (May 2017, FLAIRS 2017)

   In competitive videogame communities, a tier list is a hierarchical ranking of playable characters that, despite its simplicity, tries to capture an often nuanced metagame where matchups between characters do not follow a transitive ordering. We model the creation of tier lists as a coalition formation game, based on hedonic games, where the set of agents is partitioned into a hierarchy and an agent has preferences over the set of agents at and below its level of the hierarchy. We prove the computational complexity of determining whether there exists a stable partition under two stability notions borrowed from hedonic games. 

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4. Fundamental Bounds on Online Strategic Classification

   https://doi.org/10.1145/3580507.3597818  

   Ahmadi, Saba; Blum, Avrim; Yang, Kunhe (July 2023, Proceedings of the 24th ACM Conference on Economics and Computation)

   We study the problem of online binary classification where strategic agents can manipulate their observable features in predefined ways, modeled by a manipulation graph, in order to receive a positive classification. We show this setting differs in fundamental ways from classic (non-strategic) online classification. For instance, whereas in the non-strategic case, a mistake bound of ln |H| is achievable via the halving algorithm when the target function belongs to a known class H, we show that no deterministic algorithm can achieve a mistake bound o(Δ) in the strategic setting, where Δ is the maximum degree of the manipulation graph (even when |H| = O(Δ)). We complement this with a general algorithm achieving mistake bound O(Δ ln |H|). We also extend this to the agnostic setting, and show that this algorithm achieves a Δ multiplicative regret (mistake bound of O(Δ · OPT + Δ · ln |H|)), and that no deterministic algorithm can achieve o(Δ) multiplicative regret. Next, we study two randomized models based on whether the random choices are made before or after agents respond, and show they exhibit fundamental differences. In the first, fractional model, at each round the learner deterministically chooses a probability distribution over classifiers inducing expected values on each vertex (probabilities of being classified as positive), which the strategic agents respond to. We show that any learner in this model has to suffer linear regret. On the other hand, in the second randomized algorithms model, while the adversary who selects the next agent must respond to the learner's probability distribution over classifiers, the agent then responds to the actual hypothesis classifier drawn from this distribution. Surprisingly, we show this model is more advantageous to the learner, and we design randomized algorithms that achieve sublinear regret bounds against both oblivious and adaptive adversaries. 

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5. Sample-efficient robust multi-agent reinforcement learning in the face of environmental uncertainty

   Shi, Laixi; Mazumdar, Eric; Chi, Yuejie; Wierman, Adam (July 2024, Proceedings of the 41st International Conference on Machine Learning)

   To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied-- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DR-NVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length. 

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