Autonomous Agents and Multi-Agent Systems最新文献_第2页

Multi-stage generalized deferred acceptance mechanism: Strategyproof mechanism for handling general hereditary constraints 多阶段广义延迟接受机制：处理一般遗传约束的防策略机制

IF 2.6 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-06-27 DOI: 10.1007/s10458-025-09713-9

Kei Kimura, Kweiguu Liu, Zhaohong Sun, Kentaro Yahiro, Makoto Yokoo

{"title":"Multi-stage generalized deferred acceptance mechanism: Strategyproof mechanism for handling general hereditary constraints","authors":"Kei Kimura, Kweiguu Liu, Zhaohong Sun, Kentaro Yahiro, Makoto Yokoo","doi":"10.1007/s10458-025-09713-9","DOIUrl":"10.1007/s10458-025-09713-9","url":null,"abstract":"<div><p>The theory of two-sided matching has been extensively developed and applied to many real-life application domains. As the theory has been applied to increasingly diverse types of environments, researchers and practitioners have encountered various forms of distributional constraints. Arguably, the most general class of distributional constraints would be hereditary constraints; if a matching is feasible, then any matching that assigns weakly fewer students at each college is also feasible. However, under general hereditary constraints, it is shown that no strategyproof mechanism exists that simultaneously satisfies fairness and weak nonwastefulness, which is an efficiency (students’ welfare) requirement weaker than nonwastefulness. We propose a new strategyproof mechanism that works for hereditary constraints called the Multi-Stage Generalized Deferred Acceptance mechanism (MS-GDA). It uses the Generalized Deferred Acceptance mechanism (GDA) as a subroutine, which works when distributional constraints belong to a well-behaved class called hereditary M<span>(^{natural })</span>-convex set. We show that GDA satisfies several desirable properties, most of which are also preserved in MS-GDA. We experimentally show that MS-GDA strikes a good balance between fairness and efficiency (students’ welfare) compared to existing strategyproof mechanisms when distributional constraints are close to an M<span>(^{natural })</span>-convex set<sup>*</sup>.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-025-09713-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Cost and Complexity of Minimizing Envy in House Allocation 房屋分配中嫉妒最小化的成本和复杂性

IF 2.6 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-06-11 DOI: 10.1007/s10458-025-09710-y

Jayakrishnan Madathil, Neeldhara Misra, Aditi Sethia

{"title":"The Cost and Complexity of Minimizing Envy in House Allocation","authors":"Jayakrishnan Madathil, Neeldhara Misra, Aditi Sethia","doi":"10.1007/s10458-025-09710-y","DOIUrl":"10.1007/s10458-025-09710-y","url":null,"abstract":"<div><p>We study almost envy-freeness in house allocation, where <i>m</i> houses are to be allocated among <i>n</i> agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to settle for relaxations. We study different aggregate measures of envy as markers of fairness. In particular, we define the amount of envy experienced by an agent <i>a</i> w.r.t. an allocation to be the number of agents that agent <i>a</i> envies under that allocation. We <i>quantify</i> the envy generated by an allocation using three different metrics: 1) the number of agents who are envious; 2) the maximum amount of envy experienced by any agent; and 3) the total amount of envy experienced by all agents, and look for allocations that minimize one of the three metrics. We prove a host of algorithmic and hardness results. We also suggest practical approaches for these problems via integer linear program (ILP) formulations and report the findings of our experimental evaluation of ILPs. Finally, we study the price of fairness, which quantifies the loss of welfare we must suffer due to the fairness requirements, and present tight bounds as well as algorithms that simultaneously optimize both welfare and fairness.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 2","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi 用约定扩充动作空间，提高多agent在Hanabi中的合作

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-05-24 DOI: 10.1007/s10458-025-09709-5

Francois Bredell, Herman A. Engelbrecht, J. C. Schoeman

{"title":"Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi","authors":"Francois Bredell, Herman A. Engelbrecht, J. C. Schoeman","doi":"10.1007/s10458-025-09709-5","DOIUrl":"10.1007/s10458-025-09709-5","url":null,"abstract":"<div><p>The card game <i>Hanabi</i> is considered a strong medium for the testing and development of multi-agent reinforcement learning (MARL) algorithms, due to its cooperative nature, partial observability, limited communication and remarkable complexity. Previous research efforts have explored the capabilities of MARL algorithms within Hanabi, focusing largely on advanced architecture design and algorithmic manipulations to achieve state-of-the-art performance for various number of cooperators. However, this often leads to complex solution strategies with high computational cost and requiring large amounts of training data. For humans to solve the Hanabi game effectively, they require the use of conventions, which often allows for a means to implicitly convey ideas or knowledge based on a predefined, and mutually agreed upon, set of “rules” or principles. Multi-agent problems containing partial observability, especially when limited communication is present, can benefit greatly from the use of implicit knowledge sharing. In this paper, we propose a novel approach to augmenting an agent’s action space using <i>conventions</i>, which act as a sequence of special cooperative actions that span over and include multiple time steps and multiple agents, requiring agents to actively opt in for it to reach fruition. These <i>conventions</i> are based on existing human conventions, and result in a significant improvement on the performance of existing techniques for self-play and cross-play for various number of cooperators within Hanabi.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-025-09709-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy based blending of policies for multi-agent coexistence 基于熵的多智能体共存策略混合

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-05-16 DOI: 10.1007/s10458-025-09707-7

David Rother, Franziska Herbert, Fabian Kalter, Dorothea Koert, Joni Pajarinen, Jan Peters, Thomas H. Weisswange

{"title":"Entropy based blending of policies for multi-agent coexistence","authors":"David Rother, Franziska Herbert, Fabian Kalter, Dorothea Koert, Joni Pajarinen, Jan Peters, Thomas H. Weisswange","doi":"10.1007/s10458-025-09707-7","DOIUrl":"10.1007/s10458-025-09707-7","url":null,"abstract":"<div><p>Research on multi-agent interaction involving humans is still in its infancy. Most approaches have focused on environments with collaborative human behavior or a small, defined set of situations. When deploying robots in human-inhabited environments in the future, the diversity of interactions surpasses the capabilities of pre-trained collaboration models. ”Coexistence” environments, characterized by agents with varying or partially aligned objectives, present a unique challenge for robotic collaboration. Traditional reinforcement learning methods fall short in these settings. These approaches lack the flexibility to adapt to changing agent counts or task requirements without undergoing retraining. Moreover, existing models do not adequately support scenarios where robots should exhibit helpful behavior toward others without compromising their primary goals. To tackle this issue, we introduce a novel framework that decomposes interaction and task-solving into separate learning problems and blends the resulting policies at inference time using a goal inference model for task estimation. We create impact-aware agents and linearly scale the cost of training agents with the number of agents and available tasks. To this end, a weighting function blending action distributions for individual interactions with the original task action distribution is proposed. To support our claims we demonstrate that our framework scales in task and agent count across several environments and considers collaboration opportunities when present. The new learning paradigm opens the path to more complex multi-robot, multi-human interactions.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-025-09707-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Course Allocation with Asymmetric Friendships 基于非对称友谊的分布式课程分配

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-05-15 DOI: 10.1007/s10458-025-09708-6

Lihi Dery, Tal Grinshpoun, Ilya Khakhiashvili

{"title":"Distributed Course Allocation with Asymmetric Friendships","authors":"Lihi Dery, Tal Grinshpoun, Ilya Khakhiashvili","doi":"10.1007/s10458-025-09708-6","DOIUrl":"10.1007/s10458-025-09708-6","url":null,"abstract":"<div><p>Students’ decisions on whether to take a class are strongly affected by whether their friends plan to take the class with them. A student may prefer to be assigned to a course they like less, just to be with their friends, rather than taking a more preferred class alone. It has been shown that taking classes with friends positively affects academic performance. Thus, academic institutes should prioritize friendship relations when assigning course seats. The introduction of friendship relations results in several non-trivial changes to current course allocation methods. This paper explores how course allocation mechanisms can account for <i>friendships</i> between students and provide a unique, distributed solution. Specifically, we approach the problem by framing it as an asymmetric distributed constraint optimization problem and develop a new dedicated algorithm. Our extensive evaluation includes both simulated data and a study involving 177 students, focusing on their preferences regarding both courses and friendships. The findings indicate that our algorithm achieves significant utility for the students, maintaining fairness in the solution and adhering to the limitations on course seat capacities.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-025-09708-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: Epistemic selection of costly alternatives: the case of participatory budgeting 修正：昂贵替代方案的认知选择：参与式预算的案例

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-04-29 DOI: 10.1007/s10458-025-09702-y

Simon Rey, Ulle Endriss

引用次数: 0

Hypertension and total-order forward decomposition optimizations 高血压和全阶正分解优化

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-04-25 DOI: 10.1007/s10458-025-09705-9

Maurício Cecílio Magnaguagno, Felipe Meneguzzi, Lavindra de Silva

引用次数: 0

Improving execution concurrency in partial-order plans via block-substitution 通过块替换提高部分顺序计划的执行并发性

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-04-23 DOI: 10.1007/s10458-025-09706-8

Sabah Binte Noor, Fazlul Hasan Siddiqui

{"title":"Improving execution concurrency in partial-order plans via block-substitution","authors":"Sabah Binte Noor, Fazlul Hasan Siddiqui","doi":"10.1007/s10458-025-09706-8","DOIUrl":"10.1007/s10458-025-09706-8","url":null,"abstract":"<div><p>Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A Partial-Order Plan (POP) specifies partial-order over actions, providing the flexibility of executing unordered actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in the POP to reduce its overall execution time. While extensive studies exist on improving the flexibility of a POP by optimizing its action orderings through plan deordering and reordering, there has been limited focus on the flexibility of executing actions concurrently in a plan. Flexibility of executing actions concurrently, referred to as concurrency, in a POP can be achieved by incorporating action non-concurrency constraints, specifying which actions can not be executed in parallel. This work establishes the necessary and sufficient conditions for non-concurrency constraints between two actions or two subplans with respect to a planning task. We also introduce an algorithm to improve a plan’s concurrency by optimizing resource utilization through substitutions of the plan’s subplans with respect to the corresponding planning task. Our algorithm employs block deordering that eliminates orderings in a POP by encapsulating coherent actions in blocks, and then exploits blocks as candidate subplans for substitutions. Experiments over the benchmark problems from International Planning Competitions (IPC) exhibit considerable improvement in plan concurrency.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptation Procedure in misinformation games 错误信息游戏中的适应程序

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-21 DOI: 10.1007/s10458-025-09704-w

Konstantinos Varsos, Merkouris Papamichail, Giorgos Flouris, Marina Bitsaki

引用次数: 0

On fair and efficient solutions for budget apportionment 关于公平有效的预算分配办法

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-03-20 DOI: 10.1007/s10458-025-09694-9

Pierre Cardi, Laurent Gourvès, Julien Lesca

{"title":"On fair and efficient solutions for budget apportionment","authors":"Pierre Cardi, Laurent Gourvès, Julien Lesca","doi":"10.1007/s10458-025-09694-9","DOIUrl":"10.1007/s10458-025-09694-9","url":null,"abstract":"<div><p>This article deals with an apportionment problem involving <i>n</i> agents and a common budget <i>B</i>. Each agent submits some demands which are indivisible portions of the budget, and a central authority has to decide which demands to accept. The utility of an agent corresponds to the total amount of her accepted demands. In this context, it is desirable to be fair among the agents and efficient by not wasting the budget. An ideal solution would be to spend exactly <i>B</i>/<i>n</i> for every agent but this is rarely possible because of the indivisibility of the demands. Since combining fairness with efficiency is highly desirable but often impossible, we explore relaxed notions of fairness and efficiency, in order to determine if they go together. Our approach is also constructive because polynomial algorithms that build fair and efficient solutions are also given. The fairness criteria under consideration are the maximization of the minimum agent utility (max–min), proportionality, a customized notion of envy-freeness called jealousy-freeness, and the relaxations up to one or any demand of the previous two concepts. Efficiency in this work is either the maximization of the utilitarian social welfare or Pareto optimality. First we consider fairness and efficiency separately. The existence and computation of solutions that are either fair or efficient are studied. A complete picture of the relations that connect the fairness and efficiency concepts is provided. Second, we determine when fairness and efficiency can be combined for every possible instance. We prove that Pareto optimality is compatible with two notions of fairness, namely max–min and proportionality up to any demand. In contrast, none of the fairness concepts under consideration can be paired with the maximization of utilitarian social welfare. Therefore, we finally conduct a thorough analysis of the price of fairness which bounds the loss of efficiency caused by imposing fairness or one of its relaxations.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0