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Autonomous agents and multi-agent systems
Kluwer Acafemic Publishers
Autonomous agents and multi-agent systems

Kluwer Acafemic Publishers

1387-2532

Autonomous agents and multi-agent systems/Journal Autonomous agents and multi-agent systemsEIISTPSCIAHCI
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    An agent-based persuasion model using emotion-driven concession and multi-objective optimization

    Zhenwu WangJiayin ShenXiaosong TangMengjie Han...
    1.1-1.44页
    查看更多>>摘要：Abstract Multi-attribute negotiation is essentially a multi-objective optimization (MOO) problem, where models of agent-based emotional persuasion (EP) can exhibit characteristics of anthropomorphism. This paper proposes a novel EP model by fusing the strategy of emotion-driven concession with the method of multi-objective optimization (EDC-MOO). Firstly, a comprehensive emotion model is designed to enhance the authenticity of the emotion. A novel concession strategy is then proposed to enable the concession to be dynamically tuned by the emotions of the agents. Finally, a new EP model is constructed by integrating emotion, historical transaction, persuasion behavior, and concession strategy under the framework of MOO. Comprehensive experiments on bilateral negotiation are conducted to illustrate and validate the effectiveness of EDC-MOO. These include an analysis of negotiations under five distinct persuasion styles, a comparison of EDC-MOO with a non-emotion-based MOO negotiation model and classic trade-off strategies, negotiations between emotion-driven and non-emotion-driven agents, and negotiations involving human participants. A detailed analysis of parameter sensitivity is also discussed. Experimental results show that the proposed EDC-MOO model can enhance the diversity of the negotiation process and the anthropomorphism of the bilateral agents, thereby improving the social welfare of both parties.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    The complexity of verifying popularity and strict popularity in altruistic hedonic games

    Anna Maria KerkmannJörg Rothe
    1.1-1.31页
    查看更多>>摘要：Abstract We consider average- and min-based altruistic hedonic games and study the problem of verifying popular and strictly popular coalition structures. While strict popularity verification has been shown to be coNP-complete in min-based altruistic hedonic games, this problem has been open for equal- and altruistic-treatment average-based altruistic hedonic games. We solve these two open cases of strict popularity verification and then provide the first complexity results for popularity verification in (average- and min-based) altruistic hedonic games, where we cover all three degrees of altruism.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    GenSynthPop: generating a spatially explicit synthetic population of individuals and households from aggregated data

    Jan de MooijTabea SonnenscheinMarco PellegrinoMehdi Dastani...
    1.1-1.28页
    查看更多>>摘要：Abstract Synthetic populations are representations of actual individuals living in a specific area. They play an increasingly important role in studying and modeling individuals and are often used to build agent-based social simulations. Traditional approaches for synthesizing populations use a detailed sample of the population (which may not be available) or combine data into a single joint distribution, and draw individuals or households from these. The latter group of existing sample-free methods fail to integrate (1) the best available data on spatial granular distributions, (2) multi-variable joint distributions, and (3) household level distributions. In this paper, we propose a sample-free approach where synthetic individuals and households directly represent the estimated joint distribution to which attributes are iteratively added, conditioned on previous attributes such that the relative frequencies within each joint group of attributes are maintained and fit granular spatial marginal distributions. In this paper we present our method and test it for the Zuid-West district of The Hague, the Netherlands, showing that spatial, multi-variable and household distributions are accurately reflected in the resulting synthetic population.
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    • NETL
    • NSTL
    • Springer Nature

    A comprehensive analysis of agent factorization and learning algorithms in multiagent systems

    Andreas KallinterisStavros OrfanoudakisGeorgios Chalkiadakis
    1.1-1.48页
    查看更多>>摘要：Abstract In multiagent systems, agent factorization denotes the process of segmenting the state-action space of the environment into distinct components, each corresponding to an individual agent, and subsequently determining the interactions among these agents. Effective agent factorization significantly influences the system performance of real-world industrial applications. In this work, we try to assess the performance impact of agent factorization when using different learning algorithms in multiagent coordination settings; and thus discover the source of performance quality of the multiagent solution derived by combining different factorizations with different learning algorithms. To this end, we evaluate twelve different agent factorization instances—or agent definitions—in the warehouse traffic management domain, comparing the training performance of (primarily) three learning algorithms suitable for learning coordinated multiagent policies: the Evolutionary Strategies (ES), the Canonical Evolutionary Strategies (CES), and a genetic algorithm (CCEA) previously used in a similar setting. Our results demonstrate that the performance of different learning algorithms is affected in different ways by alternative agent definitions. Given this, we can conclude that many important multiagent coordination problems can eventually be solved more efficiently by a suitable agent factorization combined with an appropriate choice of a learning algorithm. Moreover, our work shows that ES and CES are effective learning algorithms for the warehouse traffic management domain, while, interestingly, celebrated policy gradient methods do not fare well in this complex real-world problem setting. As such, our work offers insights into the intrinsic properties of the learning algorithms that make them well-suited for this problem domain. More broadly, our work demonstrates the need to identify appropriate agent definitions-multiagent learning algorithm pairings in order to solve specific complex problems effectively, and provides insights into the general characteristics that such pairings must possess to address broad classes of multiagent learning and coordination problems.
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    • NETL
    • NSTL
    • Springer Nature

    Parameterized complexity of candidate nomination for elections based on positional scoring rules

    Ildikó SchlotterKatarína CechlárováDiana Trellová
    1.1-1.36页
    查看更多>>摘要：Abstract Consider elections where the set of candidates is partitioned into parties, and each party must nominate exactly one candidate. The Possible President problem asks whether some candidate of a given party can become the unique winner of the election for some nominations from other parties. We perform a multivariate computational complexity analysis of Possible President for several classes of elections based on positional scoring rules. We consider the following parameters: the size of the largest party, the number of parties, the number of voters and the number of voter types. We provide a complete computational map of Possible President in the sense that for each choice of the four possible parameters as (i) constant, (ii) parameter, or (iii) unbounded, we classify the computational complexity of the resulting problem as either polynomial-time solvable or NP-complete, and for parameterized versions as either fixed-parameter tractable or W[1]-hard with respect to the parameters considered.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Team-wise effective communication in multi-agent reinforcement learning

    Ming YangKaiyan ZhaoYiming WangRenzhi Dong...
    1.1-1.26页
    查看更多>>摘要：Abstract Effective communication is crucial for the success of multi-agent systems, as it promotes collaboration for attaining joint objectives and enhances competitive efforts towards individual goals. In the context of multi-agent reinforcement learning, determining “whom”, “how” and “what” to communicate are crucial factors for developing effective policies. Therefore, we propose TeamComm, a novel framework for multi-agent communication reinforcement learning. First, it introduces a dynamic team reasoning policy, allowing agents to dynamically form teams and adapt their communication partners based on task requirements and environment states in cooperative or competitive scenarios. Second, TeamComm utilizes heterogeneous communication channels consisting of intra- and inter-team to achieve diverse information flow. Lastly, TeamComm leverages the information bottleneck principle to optimize communication content, guiding agents to convey relevant and valuable information. Through experimental evaluations on three popular environments with seven different scenarios, we empirically demonstrate the superior performance of TeamComm compared to existing methods.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Multivariate algorithmics for eliminating envy by donating goods

    Niclas BoehmerRobert BredereckKlaus HeegerDušan Knop...
    1.1-1.35页
    查看更多>>摘要：Abstract Fairly dividing a set of indivisible resources to a set of agents is of utmost importance in some applications. However, after an allocation has been implemented the preferences of agents might change and envy might arise. We study the following problem to cope with such situations: given an allocation of indivisible resources to agents with additive utility-based preferences, is it possible to socially donate some of the resources (which means removing these resources from the allocation instance) such that the resulting modified allocation is envy-free (up to one good). We require that the number of deleted resources and/or the caused utilitarian welfare loss of the allocation are bounded. We conduct a thorough study of the (parameterized) computational complexity of this problem considering various natural and problem-specific parameters (e.g., the number of agents, the number of deleted resources, or the maximum number of resources assigned to an agent in the initial allocation) and different preference models, including unary-encoded and 0/1-valuations. In our studies, we obtain a rich set of (parameterized) tractability and intractability results and discover several surprising contrasts, for instance, between the two closely related fairness concepts envy-freeness and envy-freeness up to one good and between the influence of the parameters maximum number and welfare of the deleted resources.
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    • NETL
    • NSTL
    • Springer Nature

    From large language models to small logic programs: building global explanations from disagreeing local post-hoc explainers

    Andrea AgiolloLuciano Cavalcante SiebertPradeep K. MurukannaiahAndrea Omicini...
    1.1-1.33页
    查看更多>>摘要：Abstract The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Navigating in a space of game views

    Michael P. WellmanKatherine Mayo
    1.1-1.25页
    查看更多>>摘要：Abstract Game-theoretic modeling entails selecting the particular elements of a complex strategic situation deemed most salient for strategic analysis. Recognizing that any game model is one of many possible views of the situation, we term this a game view, and propose that sophisticated game reasoning would naturally consider multiple views. We introduce a conceptual framework, game view navigation, for game-theoretic reasoning through a process of constructing and analyzing a series of game views. The approach is illustrated using a variety of existing methods, which can be cast in terms of navigation patterns within this framework. By formally defining these as well as recently introduced ideas as navigating in a space of game views, we recognize common themes and opportunities for generalization. Game view navigation thus provides a unifying perspective that sheds light on connections between disparate reasoning methods, and defines a design space for creation of new techniques. We further apply the framework by defining and exploring new techniques based on modulating player aggregation in equilibrium search.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    On preferences and reward policies over rankings

    Marco FaellaLuigi Sauro
    1.1-1.36页
    查看更多>>摘要：Abstract We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature