Autonomous Agents and Multi-Agent Systems最新文献_第3页

Disagree and commit: degrees of argumentation-based agreements 不同意和承诺：基于争论的协议的程度

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-01-27 DOI: 10.1007/s10458-025-09688-7

Timotheus Kampik, Juan Carlos Nieves

引用次数: 0

Reflexive anticipatory reasoning by BDI agents BDI主体的反身性预期推理

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-01-23 DOI: 10.1007/s10458-025-09687-8

Jomi Fred Hübner, Samuele Burattini, Alessandro Ricci, Simon Mayer

引用次数: 0

Budget-feasible egalitarian allocation of conflicting jobs 预算可行的平等分配冲突的工作

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-01-11 DOI: 10.1007/s10458-024-09686-1

Sushmita Gupta, Pallavi Jain, A. Mohanapriya, Vikash Tripathi

{"title":"Budget-feasible egalitarian allocation of conflicting jobs","authors":"Sushmita Gupta, Pallavi Jain, A. Mohanapriya, Vikash Tripathi","doi":"10.1007/s10458-024-09686-1","DOIUrl":"10.1007/s10458-024-09686-1","url":null,"abstract":"<div><p>Allocating conflicting jobs among individuals while respecting a budget constraint for each individual is an optimization problem that arises in various real-world scenarios. In this paper, we consider the situation where each individual derives some satisfaction from each job. We focus on finding a feasible allocation of conflicting jobs that maximize egalitarian cost, i.e., the satisfaction of the individual who is worst-off. To the best of our knowledge, this is the first paper to combine egalitarianism, budget-feasibility, and conflict-freeness in allocations. We provide a systematic study of the computational complexity of finding budget-feasible conflict-free egalitarian allocation and show that our problem generalizes a large number of classical optimization problems. Therefore, unsurprisingly, our problem is NP-hard even for two individuals and when there is no conflict between any jobs. We show that the problem admits algorithms when studied in the realm of approximation algorithms and parameterized algorithms with a host of natural parameters that match and in some cases improve upon the running time of known algorithms.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09686-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941025","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

La VIDA: towards a motivated goal reasoning agent La VIDA：走向一个有动机的目标推理代理

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2025-01-03 DOI: 10.1007/s10458-024-09685-2

Ursula Addison

{"title":"La VIDA: towards a motivated goal reasoning agent","authors":"Ursula Addison","doi":"10.1007/s10458-024-09685-2","DOIUrl":"10.1007/s10458-024-09685-2","url":null,"abstract":"<div><p>An autonomous agent deployed to operate over extended horizons in uncertain environments will encounter situations for which it was not designed. A class of these situations involves an invalidation of agent goals and limited guidance in establishing a new set of goals to pursue. An agent will benefit from some mechanism that will allow it to pursue new goals under these circumstances such that the goals are broadly useful in its environment and take advantage of its existing skills while aligning with societal norms. We propose augmenting a goal reasoning agent, i.e., an agent that can deliberate on and self-select its goals, with a motivation system that can be used to both constrain and motivate agent behavior. A human-like motivation system coupled with a goal-self concordant selection technique allows the approach to be framed as an optimization problem in which the agent selects goals that have high utility while simultaneously in harmony with its motivations. Over the agent’s operational lifespan its motivation system adjusts incrementally to more closely reflect the reality of its goal reasoning and goal pursuit experiences. Experiments performed with an ablation testing technique comparing the average utility of goals achieved in the presence and absence of a motivation system suggest that the motivated version of the system leads to pursuing more useful goals than the baseline.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912946","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

Aggregating bipolar opinions through bipolar assumption-based argumentation 通过基于两极假设的论证汇总两极意见

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-11-25 DOI: 10.1007/s10458-024-09684-3

Charles Dickie, Stefan Lauren, Francesco Belardinelli, Antonio Rago, Francesca Toni

引用次数: 0

Information gathering in POMDPs using active inference 利用主动推理在 POMDP 中收集信息

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-11-07 DOI: 10.1007/s10458-024-09683-4

Erwin Walraven, Joris Sijs, Gertjan J. Burghouts

{"title":"Information gathering in POMDPs using active inference","authors":"Erwin Walraven, Joris Sijs, Gertjan J. Burghouts","doi":"10.1007/s10458-024-09683-4","DOIUrl":"10.1007/s10458-024-09683-4","url":null,"abstract":"<div><p>Gathering information about the environment state is the main goal in several planning tasks for autonomous agents, such as surveillance, inspection and tracking of objects. Such planning tasks are typically modeled using a Partially Observable Markov Decision Process (POMDP), and in the literature several approaches have emerged to consider information gathering during planning and execution. Similar developments can be seen in the field of active inference, which focuses on active information collection in order to be able to reach a goal. Both fields use POMDPs to model the environment, but the underlying principles for action selection are different. In this paper we create a bridge between both research fields by discussing how they relate to each other and how they can be used for information gathering. Our contribution is a tailored approach to model information gathering tasks directly in the active inference framework. A series of experiments demonstrates that our approach enables agents to gather information about the environment state. As a result, active inference becomes an alternative to common POMDP approaches for information gathering, which opens the door towards more cross cutting research at the intersection of both fields. This is advantageous, because recent advancements in POMDP solvers may be used to accelerate active inference, and the principled active inference framework may be used to model POMDP agents that operate in a neurobiologically plausible fashion.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"39 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595293","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

Ability and knowledge: from epistemic transition systems to labelled stit models 能力与知识：从认识论过渡系统到标记的 Stit 模型

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-11-06 DOI: 10.1007/s10458-024-09661-w

Alexandra Kuncová, Jan Broersen, Hein Duijf, Aldo Iván Ramírez Abarca

引用次数: 0

Epistemic selection of costly alternatives: the case of participatory budgeting 代价高昂的备选方案的认识选择：参与式预算编制案例

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-11-01 DOI: 10.1007/s10458-024-09677-2

Simon Rey, Ulle Endriss

引用次数: 0

Formal contracts mitigate social dilemmas in multi-agent reinforcement learning 正式合同缓解多代理强化学习中的社会困境

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-10-18 DOI: 10.1007/s10458-024-09682-5

Andreas Haupt, Phillip Christoffersen, Mehul Damani, Dylan Hadfield-Menell

{"title":"Formal contracts mitigate social dilemmas in multi-agent reinforcement learning","authors":"Andreas Haupt, Phillip Christoffersen, Mehul Damani, Dylan Hadfield-Menell","doi":"10.1007/s10458-024-09682-5","DOIUrl":"10.1007/s10458-024-09682-5","url":null,"abstract":"<div><p>Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all Fully Observable Markov Games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we show that for general contract spaces, and even under partial observability, richer contract spaces lead to higher welfare. Hence, contract space design solves an exploration-exploitation tradeoff, sidestepping incentive issues. We complement our theoretical analysis with experiments. Issues of exploration in the contracting augmentation are mitigated using a training methodology inspired by multi-objective reinforcement learning: Multi-Objective Contract Augmentation Learning. We test our methodology in static, single-move games, as well as dynamic domains that simulate traffic, pollution management, and common pool resource management.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09682-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447433","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

Finding middle grounds for incoherent horn expressions: the moral machine case 为不连贯的角表达寻找中间点：道德机器案例

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-10-16 DOI: 10.1007/s10458-024-09681-6

Ana Ozaki, Anum Rehman, Marija Slavkovik

引用次数: 0