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International Conference on Autonomous Agents and Multiagent Systems

作者： David Lee Ines Arana Hatem Ahriz Kit-Ying Hui School of Computing RGU

ISBN: (纸本)9781615673346

A coarse-grained distributed Constraint Satisfaction problem (DisCSP) consists of several loosely connected constraint satisfaction subproblems, each assigned to an individual agent. We present Multi-Hyb, a two-phase concurrent hybrid approach for solving DisCSPs. In the first phase, each agent's subproblem is solved using systematic search which generates the key partial solutions to the global problem. Concurrently, a penalty-based local search algorithm attempts to find a global solution from these partial solutions. If phase 1 fails to find a solution, a phase 2 systematic search algorithm solves the problem using the knowledge gained from phase 1. We show that our approach is highly competitive in comparison with other coarse-grained DisCSP algorithms.

关键词： Algorithms Artificial Intelligence Constraint Satisfaction Agent Cooperation distributed problem solving constraint satisfaction problems Artificial Intelligence distributed problem solving algorithms SEARCHING ALGORITHM

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Strategies for selecting communication structures in cooperative search

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INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS 1995年第4期4卷 405-422页

作者： Narazaki, S Yamamura, H Yoshida, N Department of Computer Science Kyushu University Fukuoka 812–81 Japan

Modeling environment is essential for agents to cooperate with each other in a distributed system. In this paper, we propose two strategies for selecting agents' communication structures in a cooperative search using their local histories as a model of their computational environment. Under the assumption of homogeneity of agents, an agent can select a proper communication structure by using a history of local computation, and the utility of communication always matches its cost. Simulations using the traveling salesman problem show that strategies produce high performance. We also describe an extension of these strategies to other areas and the means to separate them from application programs using meta-object programming in Object-Oriented Programming Languages (OOPL).

关键词： distributed problem solving cooperative search self-organization computational reflection

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A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent Systems 18

A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based...

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Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

作者： Md. Mosaddek Khan Long Tran-Thanh William Yeoh Nicholas R. Jennings University of Southampton Southampton United Kingdom Washington University in St. Louis St. Louis USA Imperial College London London United Kingdom

distributed Constraint Optimization problems (DCOPs) can be used to model a number of multi-agent coordination problems. The conventional DCOP model assumes that the subproblem that each agent is responsible for (i.e. the mapping of nodes in the constraint graph to agents) is part of the model description. While this assumption is often reasonable, there are many applications where there is some flexibility in making this assignment. In this paper, we focus on this gap and make the following contributions: (1) We formulate this problem as an optimization problem, where the goal is to find an assignment that minimizes the completion time of the DCOP algorithm (e.g. Action-GDL or Max-Sum) that operates on this mapping. (2) We propose a novel heuristic, called MNA, that can be executed in a centralized or decentralized manner. (3) Our empirical evaluation illustrates a substantial reduction in completion time, ranging from 16% to 40%, without affecting the solution quality of the algorithms, compared to the current state of the art. In addition, we observe empirically that the completion time obtained from our approach is near-optimal; it never exceeds more than 10% of what can be achieved from the optimal node-to-agent mapping.

关键词： node-to-agent mapping dcop gdl distributed problem solving

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Every Team Deserves a Second Chance: Identifying when Things Go Wrong 15

Every Team Deserves a Second Chance: Identifying when Things...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Vaishnavh Nagarajan Leandro Soriano Marcolino Milind Tambe Indian Institute of Technology Madras University of Southern California

ISBN: (纸本)9781450337700

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. We present a theoretical explanation of why our prediction method works. Further, contrary to what would be expected based on a simpler explanation using classical voting models, we argue that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent. We perform experiments in the Computer Go domain, where we obtain a high accuracy in predicting the final outcome of the games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and we show that the prediction works significantly better for a diverse team. Since our approach is domain independent, it can be easily applied to a variety of domains.

关键词： Teamwork Collective intelligence distributed problem solving Social choice theory Single and multiagent learning

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From DPS to MAS to ...: Continuing the Trends 09

From DPS to MAS to ...: Continuing the Trends

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International Conference on Autonomous Agents and Multiagent Systems

作者： Michael N. Huhns University of South Carolina Department of Computer Science and Engineering

ISBN: (纸本)9781615673346

The most important and interesting of the computing challenges we are facing are those that involve the problems and opportunities afforded by massive decentralization and disintermediation. The problems and opportunities arise in domains where controlled action is necessary, but centralized control is infeasible. These are the traditional domains of distributed problem solving and multiagent systems, and they include information systems for healthcare, commerce, energy distribution, and traffic control. However, the current incarnations of these domains are scaled well beyond anything envisioned originally. Nevertheless, traditional techniques derived from artificial intelligence are still mostly appropriate. Specifically, representation, reasoning, learning, planning, and situated semantics - when distributed computationally and extended to multiple loci of intelligence - will all be part of potential solutions. They will affect not only the ways systems will be implemented and executed in the future, but also the ways they will be designed, developed, and deployed. This paper focuses on the domains and their challenges. It describes the trends that are observable in our research technologies and shows how they can be used to confront the challenges. It is hoped that new avenues of research will be revealed from following the trends.

关键词： Algorithms Management Design Reliability MAS application description distributed and social system inspiration Cross-cutting focus INTEGRATED SHIP CONTROL domains digital power system Intelligence Phyla algorithms organisation and administration Artificial Intelligence magic angle spinning Volition Computer based information systems distributed problem solving Multi-agent systems

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MAS-DisCoSim 4 PDP: a testbed for multi-agent solutions to PDPs 10

MAS-DisCoSim 4 PDP: a testbed for multi-agent solutions to P...

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Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

作者： Jelle Van Gompel Bart Tuts Rutger Claes Mario Cruz K.U. Leuven Torres Tom Holvoet K.U. Leuven

ISBN: (纸本)9780982657119

This demo illustrates MAS-DisCoSim 4 PDP, a testbed environment for evaluating distributed multi-agent system solutions to pickup and delivery problems (PDPs). PDPs are well-studied problems in operational research, and provide a quite suitable context for testing and evaluating a wide variety of distributed solutions, using particular reasoning and/or coordination mechanisms. The testbed provides a reusable simulation framework, and a particular model for PDP *** demo illustrates the testbed by showing two MAS-based solutions, one based on gradient field coordination, a second solution based on the coordination mechanism called 'delegate MAS'. The demo is interactive: a user can select any part of a map, downloaded from the OpenStreetMap website, can run various experiments, and can change - during simulation - various problem and solution characteristics and observe their effect.

关键词： distributed problem solving tools and environments case studies and implemented systems simulation techniques

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Efficient approximate inference in distributed Bayesian networks for MAS-based sensor interpretation 08

Efficient approximate inference in distributed Bayesian netw...

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Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3

作者： Norman Carver Southern Illinois University Carbondale Carbondale IL

ISBN: (纸本)9780981738123

The multiply sectioned Bayesian network (MSBN) framework is the most studied approach for distributed Bayesian Network inference in an MAS setting. This paper describes a new framework that supports efficient approximate MAS-based sensor interpretation, more autonomy and asynchrony among the agents, and more focused, situation-specific communication patterns. Its use can lead to significant improvements in agent utilization and time-to-solution.

关键词： distributed Bayesian networks distributed problem solving

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Collective decision-making in multi-agent systems by implicit leadership 10

Collective decision-making in multi-agent systems by implici...

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Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3

作者： Chih-Han Yu Justin Werfel Radhika Nagpal Harvard University

ISBN: (纸本)9780982657133

Coordination within decentralized agent groups frequently requires reaching global consensus, but typical hierarchical approaches to reaching such decisions can be complex, slow, and not fault-tolerant. By contrast, recent studies have shown that in decentralized animal groups, a few individuals without privileged roles can guide the entire group to collective consensus on matters like travel direction. Inspired by these findings, we propose an implicit leadership algorithm for distributed multi-agent systems, which we prove reliably allows all agents to agree on a decision that can be determined by one or a few better-informed agents, through purely local sensing and interaction. The approach generalizes work on distributed consensus to cases where agents have different confidence levels in their preferred states. We present cases where informed agents share a common goal or have conflicting goals, and show how the number of informed agents and their confidence levels affects the consensus process. We further present an extension that allows for fast decision-making in a rapidly changing environment. Finally, we show how the framework can be applied to a diverse variety of applications, including mobile robot exploration, sensor network clock synchronization, and shape formation in modular robots.

关键词： multi-robot systems biologically-inspired approaches and methods distributed problem solving collective intelligence

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Ask and You Shall be Served: Representing & solving Multi-agentOptimization problems with Service Requesters and Providers 23

Ask and You Shall be Served: Representing & Solving Multi-ag...

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Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

作者： Maya Lavie Tehila Caspi Omer Lev Roie Zivan Ben Gurion University of the Negev Be'er Sheva Israel

ISBN: (纸本)9781450394321

In scenarios with numerous emergencies that arise and require the assistance of various rescue units (e.g., medical, fire, & police forces), the rescue units would ideally be allocated quickly and distributedly while aiming to minimize casualties. This is one of many examples of distributed settings with service providers (the rescue units) and service requesters (the emergencies) which we termservice oriented settings. Allocating the service providers in a distributed manner while aiming for a global optimum is hard to model, let alone achieve, using the existing distributed Constraint Optimization problem (DCOP) framework. Hence, the need for a novel approach and corresponding algorithms. We present the Service Oriented Multi-Agent Optimization problem (SOMAOP), a new framework that overcomes the shortcomings of DCOP in service oriented settings. We evaluate the framework using various algorithms based on auctions and matching algorithms (e.g., Gale Shapely). We empirically show that algorithms based on repeated auctions converge to a high quality solution very fast, while repeated matching problems converge slower, but produce higher quality solutions. We demonstrate the advantages of our approach over standard incomplete DCOP algorithms and a greedy centralized algorithm.

关键词： multi-agent optimization distributed problem solving multi-agent system distributed constraint optimization problems

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Knowledge representation, communication, and update in probability-based multiagent systems 10

Knowledge representation, communication, and update in proba...

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Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

作者： Scott Langevin University of South Carolina

ISBN: (纸本)9780982657119

The goal of this thesis is to allow easier design of probability-based agents and multiagent systems, resulting in rational decision making. A multiagent framework is presented and compared with other proposed frameworks where advantages and disadvantages of each are outlined. A central problem of message passing in probabilistic systems is the familiar rumor problem, where cycles in message passing cause redundant influence of beliefs. We develop algorithms to identify and solve the rumor problem in the context of our multiagent system. Central to our message passing scheme is the notion of soft evidential update. Traditional propagation algorithms are not compatible with soft evidence. We propose a new propagation algorithm that is based on Lazy propagation and compare the theoretical and experimental performance with other proposed solutions.

关键词： distributed problem solving communication protocols reasoning (single and multi-agent) knowledge representation

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