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Reaching consensus under a deadline Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2021-01-19 Marina Bannikova, Lihi Dery, Svetlana Obraztsova, Zinovi Rabinovich, Jeffrey S. Rosenschein
Group decisions are often complicated by a deadline. For example, in committee hiring decisions the deadline might be the next start of a budget, or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one - an option that may cost dearly. As a result, committee members might prefer to agree on a reasonable, if not necessarily the
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Towards a framework for certification of reliable autonomous systems Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-12-23 Michael Fisher, Viviana Mascardi, Kristin Yvonne Rozier, Bernd-Holger Schlingloff, Michael Winikoff, Neil Yorke-Smith
A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control. The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for
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Assisting humans in privacy management: an agent-based approach Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-12-23 A. Can Kurtan, Pınar Yolum
Image sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their
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A decentralised self-healing approach for network topology maintenance Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-11-27 Arles Rodríguez, Jonatan Gómez, Ada Diaconescu
In many distributed systems, from cloud to sensor networks, different configurations impact system performance, while strongly depending on the network topology. Hence, topological changes may entail costly reconfiguration and optimisation processes. This paper proposes a multi-agent solution for recovering networks from node failures. To preserve the network topology, the proposed approach relies
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Irrelevant matches in round-robin tournaments Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-11-13 Marco Faella, Luigi Sauro
We consider tournaments played by a set of players in order to establish a ranking among them. We introduce the notion of irrelevant match, as a match that does not influence the ultimate ranking of the involved parties. After discussing the basic properties of this notion, we seek out tournaments that have no irrelevant matches, focusing on the class of tournaments where each player challenges each
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I2RL: online inverse reinforcement learning under occlusion Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-11-05 Saurabh Arora, Prashant Doshi, Bikramjit Banerjee
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from observing its behavior on a task. It inverts RL which focuses on learning an agent’s behavior on a task based on the reward signals received. IRL is witnessing sustained attention due to promising applications in robotics, computer games, and finance, as well as in other sectors. Methods for IRL have, for
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Electric vehicle charging strategy study and the application on charging station placement Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-10-30 Yanhai Xiong, Bo An, Sarit Kraus
Optimal placement of charging stations for electric vehicles (EVs) is critical for providing convenient charging service to EV owners and promoting public acceptance of EVs. There has been a lot of work on EV charging station placement, yet EV drivers’ charging strategy, which plays an important role in deciding charging stations’ performance, is missing. EV drivers make choice among charging stations
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Efficient policy detecting and reusing for non-stationarity in Markov games Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-10-26 Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, Tianpei Yang, Yanran Li, Changjie Fan
One challenging problem in multiagent systems is to cooperate or compete with non-stationary agents that change behavior from time to time. An agent in such a non-stationary environment is usually supposed to be able to quickly detect the other agents’ policy during online interaction, and then adapt its own policy accordingly. This article studies efficient policy detecting and reusing techniques
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Logic-based technologies for multi-agent systems: a systematic literature review Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-10-19 Roberta Calegari, Giovanni Ciatto, Viviana Mascardi, Andrea Omicini
Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as
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Correction to: Control in the presence of manipulators: cooperative and competitive cases Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-10-12 Zack Fitzsimmons, Edith Hemaspaandra, Lane A. Hemaspaandra
Unfortunately, a post-galley copyediting error altered the contents of cells in the Condorcet Elections columns of the table in Footnote 7.
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Personalised rating Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-10-01 Umberto Grandi, James Stewart, Paolo Turrini
We introduce personalised rating, a network-based rating system where individuals, connected in a social network, decide whether or not to consume a service (e.g., a restaurant) based on the evaluations provided by their peers. We compare personalised rating with the more widely used objective rating where, instead, customers receive an aggregate evaluation of what everybody else has declared so far
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Interactive task learning via embodied corrective feedback Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-09-27 Mattias Appelgren, Alex Lascarides
This paper addresses a task in Interactive Task Learning (Laird et al. IEEE Intell Syst 32:6–21, 2017). The agent must learn to build towers which are constrained by rules, and whenever the agent performs an action which violates a rule the teacher provides verbal corrective feedback: e.g. “No, red blocks should be on blue blocks”. The agent must learn to build rule compliant towers from these corrections
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Stable roommates with narcissistic, single-peaked, and single-crossing preferences Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-09-11 Robert Bredereck; Jiehua Chen; Ugo Paavo Finnendahl; Rolf Niedermeier
The classical Stable Roommates problem is to decide whether there exists a matching of an even number of agents such that no two agents which are not matched to each other would prefer to be with each other rather than with their respectively assigned partners. We investigate Stable Roommates with complete (i.e., every agent can be matched with any other agent) or incomplete preferences, with ties
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Control in the presence of manipulators: cooperative and competitive cases Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-08-28 Zack Fitzsimmons; Edith Hemaspaandra; Lane A. Hemaspaandra
Control and manipulation are two of the most studied types of attacks on elections. In this paper, we study the complexity of control attacks on elections in which there are manipulators. We study both the case where the “chair” who is seeking to control the election is allied with the manipulators, and the case where the manipulators seek to thwart the chair. In the latter case, we see that the order
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Object reachability via swaps under strict and weak preferences Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-08-08 Sen Huang; Mingyu Xiao
The Housing Market problem is a widely studied resource allocation problem. In this problem, each agent can only receive a single object and has preferences over all objects. Starting from an initial endowment, we want to reach a certain assignment via a sequence of rational trades. We first consider whether an object is reachable for a given agent under a social network, where a trade between two
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A hybrid tree-based algorithm to solve asymmetric distributed constraint optimization problems Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-07-21 Dingding Chen; Yanchen Deng; Ziyu Chen; Zhongshi He; Wenxin Zhang
Asymmetric distributed constraint optimization problems (ADCOPs) have emerged as an important formalism in multi-agent community due to their ability to capture personal preferences. However, the existing search-based complete algorithms for ADCOPs only exploit local knowledge to calculate lower bounds, which leads to inefficient pruning and prohibits them from solving large scale problems. On the
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Strategic facility location problems with linear single-dipped and single-peaked preferences Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-07-18 Itai Feigenbaum; Minming Li; Jay Sethuraman; Fangzhou Wang; Shaokun Zou
We consider the design of mechanisms for locating facilities on an interval. There are multiple agents on the interval, each receiving a utility determined by their distances to the facilities. The objectives considered are maximization of social welfare (sum of utilities) and egalitarian welfare (minimum utility). Agents can misreport their locations, and so we require the mechanisms to be strategyproof—no
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Mobile Apps as Personal Assistant Agents: the JaCa-Android Framework for programming Agents-based applications on mobile devices Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-07-14 Angelo Croatti; Alessandro Ricci
A relevant application domain for agent-based software is given by mobile and wearable applications. In this context, the impressive progress of technologies in the last decade makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures, such as the Belief–Desire–Intention (BDI) one. Accordingly, in this paper we provide a comprehensive
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Specification testing of agent-based simulation using property-based testing Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-06-19 Jonathan Thaler; Peer-Olaf Siebers
The importance of Agent-Based Simulation (ABS) as scientific method to generate data for scientific models in general and for informed policy decisions in particular has been widely recognised. However, the important technique of code testing of implementations like unit testing has not generated much research interested so far. As a possible solution, in previous work we have explored the conceptual
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Obtaining costly unverifiable valuations from a single agent Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-06-19 Erel Segal-Halevi; Shani Alkoby; David Sarne
A principal needs to elicit the true value of an object she owns from an agent who has a unique ability to compute this information. The principal cannot verify the correctness of the information, so she must incentivize the agent to report truthfully. Previous works coped with this unverifiability by employing two or more information agents and awarding them according to the correlation between their
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From demonstrations to task-space specifications. Using causal analysis to extract rule parameterization from demonstrations Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-06-17 Daniel Angelov; Yordan Hristov; Subramanian Ramamoorthy
Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human–robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate
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Runtime revision of sanctions in normative multi-agent systems Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-06-16 Davide Dell’Anna; Mehdi Dastani; Fabiano Dalpiaz
To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement
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Complexity of planning for connected agents Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-06-16 Tristan Charrier; Arthur Queffelec; Ocan Sankur; François Schwarzentruber
We study a variant of the multi-agent path finding (MAPF) problem in which the group of agents are required to stay connected with a supervising base station throughout the execution. In addition, we consider the problem of covering an area with the same connectivity constraint. We show that both problems are PSPACE-complete on directed and undirected topological graphs while checking the existence
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Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-06-10 Mikko Lauri; Joni Pajarinen; Jan Peters
Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest when constant communication cannot be assumed. This is common in tasks involving information gathering with multiple independently operating sensor devices that may operate over large physical distances, such as unmanned aerial vehicles, or in communication
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A genetic algorithm based framework for local search algorithms for distributed constraint optimization problems Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-28 Ziyu Chen; Lizhen Liu; Jingyuan He; Zhepeng Yu
Local search algorithms are widely applied in solving large-scale Distributed constraint optimization problems (DCOPs) where each agent holds a value assignment to its variable and iteratively makes a decision on whether to replace its assignment according to its neighbor states. However, the value assignments of their neighbors confine their search to a small space so that agents in local search algorithms
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Picky losers and carefree winners prevail in collective risk dilemmas with partner selection Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-25 Fernando P. Santos; Samuel Mascarenhas; Francisco C. Santos; Filipa Correia; Samuel Gomes; Ana Paiva
Understanding how to design agents that sustain cooperation in multi-agent systems has been a long-lasting goal in distributed artificial intelligence. Proposed solutions rely on identifying free-riders and avoiding cooperating or interacting with them. These mechanisms of social control are traditionally studied in games with linear and deterministic payoffs, such as the prisoner’s dilemma or the
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Teammate-pattern-aware autonomy based on organizational self-design principles Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-16 Edmund H. Durfee; Abhishek Thakur; Eli Goldweber
We describe an approach for constraining robot autonomy based on the robot’s awareness of patterns of its human teammates’ behaviors, rather than either ignoring its teammates (which is fast but dangerous) or inferring their plans (which is safer but slow). We explore the promise, and limitations, of this approach in a series of simulated problems where an unmanned ground vehicle and its human teammates
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STRATA: unified framework for task assignments in large teams of heterogeneous agents Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-13 Harish Ravichandar; Kenneth Shaw; Sonia Chernova
Large teams of heterogeneous agents have the potential to solve complex multi-task problems that are intractable for a single agent working independently. However, solving complex multi-task problems requires leveraging the relative strengths of the different kinds of agents in the team. We present Stochastic TRAit-based Task Assignment (STRATA), a unified framework that models large teams of heterogeneous
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Agent programming in the cognitive era Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-12 Rafael H. Bordini; Amal El Fallah Seghrouchni; Koen Hindriks; Brian Logan; Alessandro Ricci
It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions
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The first twenty years of agent-based software development with JADE Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-11 Federico Bergenti; Giovanni Caire; Stefania Monica; Agostino Poggi
A recent survey provides convincing evidence that JADE is among the most widely used tools to develop agent-based software systems. It finds application in industrial settings and to support research, and it has been used to introduce students to software agents in various universities. This paper offers a perspective on the current state of JADE by first presenting a chronicle of the relevant events
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Interactively shaping robot behaviour with unlabeled human instructions Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-05-08 Anis Najar; Olivier Sigaud; Mohamed Chetouani
In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of instruction signals in the task-learning process, and use them simultaneously for guiding the latter. We implement our framework as a modular architecture, named TICS (Task-Instruction-Contingency-Shaping) that combines different
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Logic-based specification and verification of homogeneous dynamic multi-agent systems Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-04-28 Riccardo De Masellis; Valentin Goranko
We develop a logic-based framework for formal specification and algorithmic verification of homogeneous and dynamic concurrent multi-agent transition systems. Homogeneity means that all agents have the same available actions at any given state and the actions have the same effects regardless of which agents perform them. The state transitions are therefore determined only by the vector of numbers of
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Optimal action sequence generation for assistive agents in fixed horizon tasks Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-04-27 Kim Baraka; Francisco S. Melo; Marta Couto; Manuela Veloso
Agents providing assistance to humans are faced with the challenge of automatically adjusting the level of assistance to ensure optimal performance. In this work, we argue that identifying the right level of assistance consists in balancing positive assistance outcomes and some (domain-dependent) measure of cost associated with assistive actions. Towards this goal, we contribute a general mathematical
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Learning multi-agent communication with double attentional deep reinforcement learning Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-03-25 Hangyu Mao; Zhengchao Zhang; Zhen Xiao; Zhibo Gong; Yan Ni
Communication is a critical factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been adopted to learn the communication among multiple intelligent agents. However, in terms of the DRL setting, the increasing number of communication messages introduces two problems: (1) there are usually some redundant messages; (2) even in the case
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Strategic voting in the lab: compromise and leader bias behavior Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-03-11 Reshef Meir; Kobi Gal; Maor Tal
Plurality voting is perhaps the most commonly used way to aggregate the preferences of multiple voters. Yet, there is no consensus on how people vote strategically, even in very simple settings. The purpose of this paper is to provide a comprehensive study of people’s voting behavior in various online settings under the plurality rule. We implemented voting games that replicate two common real-world
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Agent Based Modelling and Simulation to estimate movement time of pilgrims from one place to another at Allahabad Jn. Railway Station during Kumbh Mela-2019 Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-03-09 Abha Trivedi; Mayank Pandey
Kumbh Mela festival of India is one of the largest mass gathering event of huge religious importance all over the world. Large gatherings in these kind of religious events require rigorous monitoring and attention. Successful organization of such events requires synchronization among officials of different public departments such as police, health, security, communication, railways etc. The railway
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An anytime algorithm for optimal simultaneous coalition structure generation and assignment Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-03-03 Fredrik Präntare; Fredrik Heintz
An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the system’s performance—the agents can, in many instances, be organized so that they can solve tasks more efficiently, and consequently benefit collectively and individually
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Model primitives for hierarchical lifelong reinforcement learning Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-25 Bohan Wu; Jayesh K. Gupta; Mykel Kochenderfer
Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult. Such decomposition can lead to immense sample efficiency gains in lifelong learning. Some traditional hierarchical reinforcement learning techniques enforce this decomposition in a top-down manner, while meta-learning techniques require a task distribution at hand to learn
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Gehrlein stability in committee selection: parameterized hardness and algorithms Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-24 Sushmita Gupta; Pallavi Jain; Sanjukta Roy; Saket Saurabh; Meirav Zehavi
In a multiwinner election based on the Condorcet criterion, we are given a set of candidates, and a set of voters with strict preference rankings over the candidates. A committee is weakly Gehrlein stable (WGS) if each committee member is preferred to each non-member by at least half of the voters. Recently, Aziz et al. [IJCAI 2017] studied the computational complexity of finding a WGS committee of
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Competitive equilibrium for almost all incomes: existence and fairness Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-20 Erel Segal-Halevi
Competitive equilibrium (CE) is a fundamental concept in market economics. Its efficiency and fairness properties make it particularly appealing as a rule for fair allocation of resources among agents with possibly different entitlements. However, when the resources are indivisible, a CE might not exist even when there is one resource and two agents with equal incomes. Recently, Babaioff and Nisan
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Two approximation algorithms for probabilistic coalition structure generation with quality bound Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-18 Kouki Matsumura; Bojana Kodric; Tenda Okimoto; Katsutoshi Hirayama
How to form effective coalitions is an important issue in multi-agent systems. Coalition Structure Generation (\({{\mathsf {CSG}}}\)) is a fundamental problem whose formalization can encompass various applications related to multi-agent cooperation. \({{\mathsf {CSG}}}\) involves partitioning a set of agents into coalitions such that the social surplus (i.e., the sum of the values of all coalitions)
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Computing and testing Pareto optimal committees Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-17 Haris Aziz; Jérôme Monnot
Selecting a set of alternatives based on the preferences of agents is an important problem in committee selection and beyond. Among the various criteria put forth for desirability of a committee, Pareto optimality is a minimal and important requirement. As asking agents to specify their preferences over exponentially many subsets of alternatives is practically infeasible, we assume that each agent
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Strategyproof and fair matching mechanism for ratio constraints Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-14 Kentaro Yahiro; Yuzhe Zhang; Nathanaël Barrot; Makoto Yokoo
We introduce a new type of distributional constraints called ratio constraints, which explicitly specify the required balance among schools in two-sided matching. Since ratio constraints do not belong to the known well-behaved class of constraints called M-convex set, developing a fair and strategyproof mechanism that can handle them is challenging. We develop a novel mechanism called quota reduction
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Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-02-12 Guangliang Li; Hamdi Dibeklioğlu; Shimon Whiteson; Hayley Hung
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this article, we investigate the potential of agent learning from trainers’ facial expressions via interpreting them as evaluative feedback. To do so, we implemented
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The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-21 Jen Jen Chung; Damjan Miklić; Lorenzo Sabattini; Kagan Tumer; Roland Siegwart
The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team
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A norm enforcement mechanism for a time-constrained conditional normative framework Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-20 B. O. Akinkunmi; Florence M. Babalola
This paper presents the formalization for a system that monitors and enforces regulative time-constrained conditional norms through sanctioning, for agent societies. The representation here has the advantage of allowing for qualitative and quantitative interval-based temporal constraints between a norm’s condition and its effect. The system possesses mechanisms for monitoring both norm compliance and
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Semantics and algorithms for trustworthy commitment achievement under model uncertainty Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-18 Qi Zhang; Edmund H. Durfee; Satinder Singh
We focus on how an agent can exercise autonomy while still dependably fulfilling commitments it has made to another, despite uncertainty about outcomes of its actions and how its own objectives might evolve. Our formal semantics treats a probabilistic commitment as constraints on the actions an autonomous agent can take, rather than as promises about states of the environment it will achieve. We have
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Fast core pricing algorithms for path auction Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-14 Hao Cheng; Wentao Zhang; Yi Zhang; Lei Zhang; Jun Wu; Chongjun Wang
Path auction is held in a graph, where each edge stands for a commodity and the weight of this edge represents the prime cost. Bidders own some edges and make bids for their edges. The auctioneer needs to purchase a sequence of edges to form a path between two specific vertices. Path auction can be considered as a kind of combinatorial reverse auctions. Core-selecting mechanism is a prevalent mechanism
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A collaborative agent-based traffic signal system for highly dynamic traffic conditions Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-13 Behnam Torabi; Rym Z. Wenkstern; Robert Saylor
In this paper we present DALI, a distributed, collaborative multi-agent traffic signal timing system (TST) for highly dynamic traffic conditions. In DALI, intersection controllers are augmented with software agents which collaboratively adapt signal timings by considering the feedback of all controller agents that may be affected by a change. The model is based on a real-world TST and will be deployed
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Crossmodal attentive skill learner: learning in Atari and beyond with audio–video inputs Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-13 Dong-Ki Kim; Shayegan Omidshafiei; Jason Pazis; Jonathan P. How
This paper introduces the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic architecture [Harb et al. in When waiting is not an option: learning options with a deliberation cost. arXiv preprint arXiv:1709.04571, 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. Agents trained using our approach learn
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Privacy sensitive environment re-decomposition for junction tree agent organization construction Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-10 Yang Xiang; Abdulrahman Alshememry
A number of frameworks for decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning assume a junction tree agent organization (JT-org). A natural decomposition of agent environment may not admit a JT-org. Hence, JT-org construction involves three related tasks: (1) Recognize whether a JT-org exists for a given environment decomposition. (2) When JT-orgs exist, construct
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COMBIMA: truthful, budget maintaining, dynamic combinatorial market Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-04 Rica Gonen; Ozi Egri
Current interest in two-sided markets is motivated by examples of successful practical applications of market mechanisms in supply chain markets, online advertising exchanges, and pollution-rights markets. Many of these examples require markets where agents arrive dynamically and can trade multiple commodities. However, the known literature largely focuses on settings with single-commodity unit demand
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Applying Max-sum to asymmetric distributed constraint optimization problems Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2020-01-01 Roie Zivan; Tomer Parash; Liel Cohen-Lavi; Yarden Naveh
We study the adjustment and use of the Max-sum algorithm for solving Asymmetric Distributed Constraint Optimization Problems (ADCOPs). First, we formalize asymmetric factor-graphs and apply the different versions of Max-sum to them. Apparently, in contrast to local search algorithms, most Max-sum versions perform similarly when solving symmetric and asymmetric problems and some even perform better
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Solving the fair electric load shedding problem in developing countries Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-11 Olabambo Ifeoluwa Oluwasuji; Obaid Malik; Jie Zhang; Sarvapali Dyanand Ramchurn
Often because of limitations in generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves households in disconnected parts without electricity, causing them inconvenience and discomfort. Without fairness being taken into due consideration during load shedding, some households
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Multi-objective multi-agent decision making: a utility-based analysis and survey Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-09 Roxana Rădulescu; Patrick Mannion; Diederik M. Roijers; Ann Nowé
The majority of multi-agent system implementations aim to optimise agents’ policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed
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The complexity of bribery and control in group identification Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-09 Gábor Erdélyi; Christian Reger; Yongjie Yang
The goal of this paper is to analyze the complexity of constructive/destructive bribery and destructive control in the framework of group identification. Group identification applies to situations where a group of individuals determine who among them are socially qualified. We consider consent rules, the consensus-start-respecting rule, and the liberal-start-respecting rule. Each consent rule is characterized
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Partition decision trees: representation for efficient computation of the Shapley value extended to games with externalities Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-09 Oskar Skibski; Tomasz P. Michalak; Yuko Sakurai; Michael Wooldridge; Makoto Yokoo
While coalitional games with externalities model a variety of real-life scenarios of interest to computer science, they pose significant game-theoretic and computational challenges. Specifically, key game-theoretic solution concepts—and the Shapley value in particular—can be extended to games with externalities in multiple, often orthogonal, ways. As for the computational challenges, while there exist
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Agents teaching agents: a survey on inter-agent transfer learning Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-09 Felipe Leno Da Silva; Garrett Warnell; Anna Helena Reali Costa; Peter Stone
While recent work in reinforcement learning (RL) has led to agents capable of solving increasingly complex tasks, the issue of high sample complexity is still a major concern. This issue has motivated the development of additional techniques that augment RL methods in an attempt to increase task learning speed. In particular, inter-agent teaching—endowing agents with the ability to respond to instructions
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Strategic negotiations for extensive-form games Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-04 Dave de Jonge; Dongmo Zhang
When studying extensive-form games it is commonly assumed that players make their decisions individually. One usually does not allow the possibility for the players to negotiate their respective strategies and formally commit themselves to future moves. As a consequence, many non-zero-sum games have been shown to have equilibrium outcomes that are suboptimal and arguably counter-intuitive. For this
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Stable outcomes in modified fractional hedonic games Auton. Agent. Multi-Agent Syst. (IF 1.342) Pub Date : 2019-12-04 Gianpiero Monaco; Luca Moscardelli; Yllka Velaj
In coalition formation games self-organized coalitions are created as a result of the strategic interactions of independent agents. In this paper we assume that for each couple of agents (i, j), weight \(w_{i,j}=w_{j,i}\) reflects how much agents i and j benefit from belonging to the same coalition. We consider the (symmetric) modified fractional hedonic game, that is a coalition formation game in