Autonomous Agents and Multi-Agent Systems最新文献

{"title":"Navigating in a space of game views","authors":"Michael P. Wellman,&nbsp;Katherine Mayo","doi":"10.1007/s10458-024-09660-x","DOIUrl":"10.1007/s10458-024-09660-x","url":null,"abstract":"<div><p>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 <i>game view</i>, and propose that sophisticated game reasoning would naturally consider multiple views. We introduce a conceptual framework, <i>game view navigation</i>, 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.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571668","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}

{"title":"Beyond the echo chamber: modelling open-mindedness in citizens’ assemblies","authors":"Jake Barrett,&nbsp;Kobi Gal,&nbsp;Loizos Michael,&nbsp;Dan Vilenchik","doi":"10.1007/s10458-024-09655-8","DOIUrl":"10.1007/s10458-024-09655-8","url":null,"abstract":"<div><p>A Citizens’ assembly (CA) is a democratic innovation tool where a randomly selected group of citizens deliberate a topic over multiple rounds to generate, and then vote upon, policy recommendations. Despite growing popularity, little work exists on understanding how CA inputs, such as the expert selection process and the mixing method used for discussion groups, affect results. In this work, we model CA deliberation and opinion change as a multi-agent systems problem. We introduce and formalise a set of criteria for evaluating successful CAs using insight from previous CA trials and theoretical results. Although real-world trials meet these criteria, we show that finding a model that does so is non-trivial; through simulations and theoretical arguments, we show that established opinion change models fail at least one of these criteria. We therefore propose an augmented opinion change model with a latent ‘open-mindedness’ variable, which sufficiently captures people’s propensity to change opinion. We show that data from the CA of Scotland indicates a latent variable both exists and resembles the concept of open-mindedness in the literature. We calibrate parameters against real CA data, demonstrating our model’s ecological validity, before running simulations across a range of realistic global parameters, with each simulation satisfying our criteria. Specifically, simulations meet criteria regardless of expert selection, expert ordering, participant extremism, and sub-optimal participant grouping, which has ramifications for optimised algorithmic approaches in the computational CA space.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09655-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520821","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}

{"title":"On preferences and reward policies over rankings","authors":"Marco Faella,&nbsp;Luigi Sauro","doi":"10.1007/s10458-024-09656-7","DOIUrl":"10.1007/s10458-024-09656-7","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09656-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520822","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}

{"title":"Parameterized complexity of candidate nomination for elections based on positional scoring rules","authors":"Ildikó Schlotter,&nbsp;Katarína Cechlárová,&nbsp;Diana Trellová","doi":"10.1007/s10458-024-09658-5","DOIUrl":"10.1007/s10458-024-09658-5","url":null,"abstract":"<div><p>Consider elections where the set of candidates is partitioned into parties, and each party must nominate exactly one candidate. The P<span>ossible</span> P<span>resident</span> 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 P<span>ossible</span> P<span>resident</span> 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 P<span>ossible</span> P<span>resident</span> 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 <span>NP</span>-complete, and for parameterized versions as either fixed-parameter tractable or <span>W</span>[1]-hard with respect to the parameters considered.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09658-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506306","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}

{"title":"A comprehensive analysis of agent factorization and learning algorithms in multiagent systems","authors":"Andreas Kallinteris,&nbsp;Stavros Orfanoudakis,&nbsp;Georgios Chalkiadakis","doi":"10.1007/s10458-024-09662-9","DOIUrl":"10.1007/s10458-024-09662-9","url":null,"abstract":"<div><p>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 <i>agent definitions</i>—in the warehouse traffic management domain, comparing the training performance of (primarily) three learning algorithms suitable for learning coordinated multiagent policies: the Evolutionary Strategies (<i>ES</i>), the Canonical Evolutionary Strategies (<i>CES</i>), and a genetic algorithm (<i>CCEA</i>) 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.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506307","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}

{"title":"Assimilating human feedback from autonomous vehicle interaction in reinforcement learning models","authors":"Richard Fox,&nbsp;Elliot A. Ludvig","doi":"10.1007/s10458-024-09659-4","DOIUrl":"10.1007/s10458-024-09659-4","url":null,"abstract":"<div><p>A significant challenge for real-world automated vehicles (AVs) is their interaction with human pedestrians. This paper develops a methodology to directly elicit the AV behaviour pedestrians find suitable by collecting quantitative data that can be used to measure and improve an algorithm's performance. Starting with a Deep Q Network (DQN) trained on a simple Pygame/Python-based pedestrian crossing environment, the reward structure was adapted to allow adjustment by human feedback. Feedback was collected by eliciting behavioural judgements collected from people in a controlled environment. The reward was shaped by the inter-action vector, decomposed into feature aspects for relevant behaviours, thereby facilitating both implicit preference selection and explicit task discovery in tandem. Using computational RL and behavioural-science techniques, we harness a formal iterative feedback loop where the rewards were repeatedly adapted based on human behavioural judgments. Experiments were conducted with 124 participants that showed strong initial improvement in the judgement of AV behaviours with the adaptive reward structure. The results indicate that the primary avenue for enhancing vehicle behaviour lies in the predictability of its movements when introduced. More broadly, recognising AV behaviours that receive favourable human judgments can pave the way for enhanced performance.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09659-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506320","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}

{"title":"Correction: Warmth and competence in human-agent cooperation","authors":"Kevin R. McKee,&nbsp;Xuechunzi Bai,&nbsp;Susan T. Fiske","doi":"10.1007/s10458-024-09654-9","DOIUrl":"10.1007/s10458-024-09654-9","url":null,"abstract":"","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09654-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141398612","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}

{"title":"A framework for trust-related knowledge transfer in human–robot interaction","authors":"Mohammed Diab,&nbsp;Yiannis Demiris","doi":"10.1007/s10458-024-09653-w","DOIUrl":"10.1007/s10458-024-09653-w","url":null,"abstract":"<div><p>Trustworthy human–robot interaction (HRI) during activities of daily living (ADL) presents an interesting and challenging domain for assistive robots, particularly since methods for estimating the trust level of a human participant towards the assistive robot are still in their infancy. Trust is a multifaced concept which is affected by the interactions between the robot and the human, and depends, among other factors, on the history of the robot’s functionality, the task and the environmental state. In this paper, we are concerned with the challenge of trust transfer, i.e. whether experiences from interactions on a previous collaborative task can be taken into consideration in the trust level inference for a new collaborative task. This has the potential of avoiding re-computing trust levels from scratch for every new situation. The key challenge here is to automatically evaluate the similarity between the original and the novel situation, then adapt the robot’s behaviour to the novel situation using previous experience with various objects and tasks. To achieve this, we measure the semantic similarity between concepts in knowledge graphs (KGs) and adapt the robot’s actions towards a specific user based on personalised interaction histories. These actions are grounded and then verified before execution using a geometric motion planner to generate feasible trajectories in novel situations. This framework has been experimentally tested in human–robot handover tasks in different kitchen scene contexts. We conclude that trust-related knowledge positively influences and improves collaboration in both performance and time aspects.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09653-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141168330","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}

{"title":"Warmth and competence in human-agent cooperation","authors":"Kevin R. McKee,&nbsp;Xuechunzi Bai,&nbsp;Susan T. Fiske","doi":"10.1007/s10458-024-09649-6","DOIUrl":"10.1007/s10458-024-09649-6","url":null,"abstract":"<div><p>Interaction and cooperation with humans are overarching aspirations of artificial intelligence research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through “objective” metrics such as task performance, obscuring potential variation in the levels of trust and subjective preference that different agents garner. To better understand the factors shaping subjective preferences in human-agent cooperation, we train deep reinforcement learning agents in Coins, a two-player social dilemma. We recruit <span>(N = 501)</span> participants for a human-agent cooperation study and measure their impressions of the agents they encounter. Participants’ perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics. Drawing inspiration from social science and biology research, we subsequently implement a new “partner choice” framework to elicit <i>revealed</i> preferences: after playing an episode with an agent, participants are asked whether they would like to play the next episode with the same agent or to play alone. As with stated preferences, social perception better predicts participants’ revealed preferences than does objective performance. Given these results, we recommend human-agent interaction researchers routinely incorporate the measurement of social perception and subjective preferences into their studies.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09649-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141147269","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}

{"title":"Majority opinion diffusion: when tie-breaking rule matters","authors":"Ahad N. Zehmakan","doi":"10.1007/s10458-024-09651-y","DOIUrl":"10.1007/s10458-024-09651-y","url":null,"abstract":"<div><p>Consider a graph <i>G</i>, which represents a social network, and assume that initially each node is either blue or white (corresponding to its opinion on a certain topic). In each round, all nodes simultaneously update their color to the most frequent color in their neighborhood. This is called the Majority Model (MM) if a node keeps its color in case of a tie and the Random Majority Model (RMM) if it chooses blue with probability 1/2 and white otherwise. We study the convergence properties of the above models, including stabilization time, periodicity, and the number of stable configurations. In particular, we prove that the stabilization time in RMM can be exponential in the size of the graph, which is in contrast with the previously known polynomial bound on the stabilization time of MM. We provide some bounds on the minimum size of a winning set, which is a set of nodes whose agreement on a color in the initial coloring enforces the process to end in a coloring where all nodes share that color. Furthermore, we calculate the expected final number of blue nodes for a random initial coloring, where each node is colored blue independently with some fixed probability, on cycle graphs. Finally, we conduct some experiments which complement our theoretical findings and also let us investigate other aspects of the models.</p></div>","PeriodicalId":55586,"journal":{"name":"Autonomous Agents and Multi-Agent Systems","volume":"38 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10458-024-09651-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121750","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}