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

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

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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

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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

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Resolving social dilemmas with minimal reward transfer 以最小的报酬转移解决社会困境

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-10-12 DOI: 10.1007/s10458-024-09675-4

Richard Willis, Yali Du, Joel Z. Leibo, Michael Luck

{"title":"Resolving social dilemmas with minimal reward transfer","authors":"Richard Willis, Yali Du, Joel Z. Leibo, Michael Luck","doi":"10.1007/s10458-024-09675-4","DOIUrl":"10.1007/s10458-024-09675-4","url":null,"abstract":"<div><p>Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In response, we formalise social dilemmas and introduce a novel metric, the <i>general self-interest level</i>, to quantify the disparity between individual and group rationality in such scenarios. This metric represents the maximum proportion of their individual rewards that agents can retain while ensuring that a social welfare optimum becomes a dominant strategy. Our approach diverges from traditional concepts of altruism, instead focusing on strategic reward redistribution. By transferring rewards among agents in a manner that aligns individual and group incentives, rational agents will maximise collective welfare while pursuing their own interests. We provide an algorithm to compute efficient transfer structures for an arbitrary number of agents, and introduce novel multi-player social dilemma games to illustrate the effectiveness of our method. This work provides both a descriptive tool for analysing social dilemmas and a prescriptive solution for resolving them via efficient reward transfer contracts. Applications include mechanism design, where we can assess the impact on collaborative behaviour of modifications to models of environments.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09675-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411510","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

GenSynthPop: generating a spatially explicit synthetic population of individuals and households from aggregated data GenSynthPop：从聚合数据中生成空间明确的个人和家庭合成人口

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-10-03 DOI: 10.1007/s10458-024-09680-7

Jan de Mooij, Tabea Sonnenschein, Marco Pellegrino, Mehdi Dastani, Dick Ettema, Brian Logan, Judith A. Verstegen

{"title":"GenSynthPop: generating a spatially explicit synthetic population of individuals and households from aggregated data","authors":"Jan de Mooij, Tabea Sonnenschein, Marco Pellegrino, Mehdi Dastani, Dick Ettema, Brian Logan, Judith A. Verstegen","doi":"10.1007/s10458-024-09680-7","DOIUrl":"10.1007/s10458-024-09680-7","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09680-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409656","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 complexity of verifying popularity and strict popularity in altruistic hedonic games 利他主义享乐游戏中验证受欢迎程度和严格受欢迎程度的复杂性

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-10-01 DOI: 10.1007/s10458-024-09679-0

Anna Maria Kerkmann, Jörg Rothe

引用次数: 0

Theoretical properties of the MiCRO negotiation strategy 米克罗谈判战略的理论特性

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-09-30 DOI: 10.1007/s10458-024-09678-1

Dave de Jonge

{"title":"Theoretical properties of the MiCRO negotiation strategy","authors":"Dave de Jonge","doi":"10.1007/s10458-024-09678-1","DOIUrl":"10.1007/s10458-024-09678-1","url":null,"abstract":"<div><p>Recently, we have introduced a new algorithm for automated negotiation, called MiCRO, which, despite its simplicity, outperforms many state-of-the-art negotiation strategies (de Jonge, in: Raedt (ed) Proceedings of the thirty-first international joint conference on artificial intelligence, ijcai.org, Vienna, Austria, 2022). Furthermore, we claimed that under certain conditions which typically hold in the Automated Negotiating Agents Competition (ANAC), it is a game-theoretically optimal strategy. The goal of this paper is to formally prove those claims. Specifically, we define ‘negotiation’ as an extensive-form game and define the class of <i>consistent</i> strategies for this game, which consists of those strategies that satisfy a number of rationality criteria. We then prove that under the above mentioned conditions MiCRO is a best response against itself among all consistent negotiation strategies. Furthermore, we define the notion of a <i>balanced</i> negotiation domain, which is a domain in which two MiCRO agents would always come to an optimal agreement. Finally, we show that many of the domains used in ANAC indeed happen to be (approximately) balanced. The importance of this work is that if we know under which conditions MiCRO is theoretically optimal, then we can use this to test to what extent other negotiation algorithms are able to achieve similar results to MiCRO when applied under those same conditions. Furthermore, it would help researchers to design more challenging test cases for automated negotiation in which MiCRO is not optimal.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09678-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415178","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

Personalised electric vehicle charging stop planning through online estimators 通过在线估算器进行个性化电动汽车充电桩规划

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-09-30 DOI: 10.1007/s10458-024-09671-8

Elnaz Shafipour, Sebastian Stein, Selin Ahipasaoglu

{"title":"Personalised electric vehicle charging stop planning through online estimators","authors":"Elnaz Shafipour, Sebastian Stein, Selin Ahipasaoglu","doi":"10.1007/s10458-024-09671-8","DOIUrl":"10.1007/s10458-024-09671-8","url":null,"abstract":"<div><p>In this paper, we address the problem of finding charging stops while travelling in electric vehicles (EVs) using artificial intelligence (AI). Choosing a charging station is challenging, because drivers have very heterogeneous preferences in terms of how they trade off the features of various alternatives (for example, regarding the time spent driving, charging costs, waiting times at charging stations, and the facilities provided at the charging stations). The key problem here is eliciting the diverse preferences of drivers, assuming that these preferences are typically not fully known a priori, and then planning stops based on each driver’s preferences. Our approach to solving this problem is to develop an intelligent personal agent that learns preferences gradually over multiple interactions. This study proposes a new technique that utilises a small-scale discrete choice experiment as a method of interacting with the driver in order to minimise the cognitive burden on the driver. Using this method, drivers are presented with a variety of routes with possible combinations of charging stops depending on the agent’s latest belief about their preferences. In subsequent iterations, the personal agent will continue to learn and refine its belief about the driver’s preferences, suggesting more personalised routes that are closer to the driver’s preferences. Based on real preference data from EV drivers, we evaluate our novel algorithm and show that, after only a few queries, our method quickly converges to the optimal routes for EV drivers [This paper is an extended version of an ECAI workshop short paper (Shafipour Yourdshahi et al., in: ECAI 2023 workshops, Kraków, Poland, 2023)].</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09671-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415137","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

Strategic manipulation of preferences in the rank minimization mechanism 在等级最小化机制中对偏好的策略性操纵

IF 2 3区计算机科学

Autonomous Agents and Multi-Agent Systems Pub Date : 2024-09-24 DOI: 10.1007/s10458-024-09676-3

Mayesha Tasnim, Youri Weesie, Sennay Ghebreab, Max Baak

{"title":"Strategic manipulation of preferences in the rank minimization mechanism","authors":"Mayesha Tasnim, Youri Weesie, Sennay Ghebreab, Max Baak","doi":"10.1007/s10458-024-09676-3","DOIUrl":"10.1007/s10458-024-09676-3","url":null,"abstract":"<div><p>We consider one-sided matching problems, where agents are allocated items based on stated preferences. Posing this as an assignment problem, the average rank of obtained matchings can be minimized using the rank minimization (RM) mechanism. RM matchings can have significantly better rank distributions than matchings obtained by mechanisms with random priority, such as Random Serial Dictatorship. However, these matchings are sensitive to preference manipulation from strategic agents. In this work we consider a scenario where agents aim to be matched to their top-<i>n</i> preferred items using the RM mechanism, and strategically manipulate their preferences to achieve this. We derive a best response strategy for an agent to be assigned to their <i>n</i> most preferred items using the Hungarian algorithm, under a simplified cost function. This strategy is then extended to a first-order heuristic strategy for being matched to the top-<i>n</i> items in a setup that minimizes the average rank. Based on this finding, an empirical study is conducted examining the impact of the first-order heuristic strategy. The study utilizes data from both simulated markets and real-world matching markets in Amsterdam, taking into account variations in item popularity, fractions of strategic agents, and the preferences for the <i>n</i> most favored items. For most scenarios, RM yields more rank efficient matches than Random Serial Dictatorship, even when agents apply the first-order heuristic strategy. However, although highly market dependent, the matching performance can become worse when 50% of agents or more want to be matched to their top-1 or top-2 preferred items and apply the first-order heuristic strategy to achieve this.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09676-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413494","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