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An Algorithm for the Myerson Value in Probabilistic Graphs with an Application to Weighted Voting

IEEE INTELLIGENT SYSTEMS 2017年第1期32卷 32-39页

作者： Skibski, Oskar Yokoo, Makoto Univ Warsaw Inst Informat PL-00325 Warsaw Poland Kyushu Univ Grad Sch Informat Sci & Elect Engn Dept Intelligent Syst Fukuoka 812 Japan

Myerson"s graph-restricted games are a well-known formalism for modeling cooperation that"s subject to restrictions. In particular, Myerson considered a coalitional game in which cooperation is possible only through an underlying network of links between agents. A unique fair solution concept for graph-restricted games is called the Myerson value. One study generalized these results by considering probabilistic graphs in which agents can cooperate via links only to some extent, that is, with some probability. The authors" algorithm is based on the enumeration of all connected subgraphs in the graph. As a sample application of the new algorithm, they consider a probabilistic graph that represents likelihood of pairwise collaboration between political parties before the 2015 general elections in the UK.

关键词： Game Theory Graph Theory Politics Probability Probabilistic Graphs Weighted Voting Application Myerson Graph Restricted Games Coalitional Game Graph Restricted Games Myerson Value Subgraphs Political Parties UK Games Probabilistic Logic Computational Modeling Mathematical Model Economics Intelligent Systems Economics Social And Behavioral Sciences Computer Applications Multiagent Systems distributed artificial intelligence artificial intelligence Computing Methodologies Intelligent Systems

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Service composition based on multi-agent in the cooperative game

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 68卷 128-135页

作者： Yu Lei Zhang Junxing Inner Mongolia Univ Hohhot Peoples R China BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China

The principle of service composition based on multi-agent is that multi-agent can coordinate to reach Pareto-optimal Nash equilibrium. Reinforcement learning algorithms can be used to deal with the coordination problem in cooperative games. In this paper, the multi-agent coordination problems in cooperative games for different user preference is investigated. In our case, each agent can represent a user's preference, and it finally learns a policy that is best fit for that user. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). In addition, user preference should be considered. To avoid local optimal solution, we let each agent randomly change interacting partners in each iteration. Thus, an agent can learn its optimal strategy by interacting repeatedly with the rest of agents representing different user preference. The experimental results show that our reinforcement learning algorithm' outperforms other learning methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed artificial intelligence Cooperative games Multi-agent coordination QoS Reinforcement learning

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A distributed problem-solving approach for owner-contractor prequalification

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ANNALS OF OPERATIONS RESEARCH 1998年第0期78卷 111-125页

作者： Park, SC Russell, JS Taha, MA Korea Adv Inst Sci & Technol Dept Ind Management Yusong Gu Taejon 305701 South Korea Univ Wisconsin Dept Civil & Environm Engn Madison WI 53706 USA

This paper reports the results of work that aims to study the practical implementation of distributed artificial intelligence (DAI) and to introduce its capabilities in representing and using knowledge in the area of contractor prequalification. This is accomplished by developing an intelligent Knowledge-Based Decision Support System for a contractor prequalification environment. The resulting DAI architecture comprises a hierarchy of loosely coupled problem solvers, all operating under the supervision of a top-level control mechanism. Each problem solver works as a classifier system. The problem-solving knowledge for each problem solver is developed using (1) machine learning by training neural networks, and (2) heuristic rules of thumb. A learning and refining subsystem is attached to the system for refining the existing knowledge to improve the system performance.

关键词： distributed artificial intelligence Knowledge-Based Decision Support System machine learning heuristic contractor prequalification

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Evolutionary learning of communicating agents

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INFORMATION SCIENCES 1998年第1-4期108卷 181-205页

作者： Iba, H Electrotech Lab Machine Inference Sect Tsukuba Ibaraki 305 Japan

This paper presents the emergence of the cooperative behavior for communicating agents by means of Genetic Programming (GP). Our experimental domains are the pursuit game and the robot navigation task. We conduct experiments with the evolution of the communicating agents and show the effectiveness of the emergent communication in terms of the robustness of generated GP programs. The performance of GP-based multi-agent learning is discussed with comparative experiments by using different breeding strategies, i.e., homogenous breeding and heterogeneous breeding. (C) 1998 Elsevier Science Inc. All rights reserved.

关键词： genetic programming multi-agent system distributed artificial intelligence

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artificial Neural Network Modeling for Airline Disruption Management

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第5期19卷 382-393页

作者： Ogunsina, Kolawole Okolo, Wendy A. Purdue Univ Sch Aeronaut & Astronaut 701 Stadium Avenue W Lafayette IN 47907 USA NASA Ames Res Ctr Intelligent Syst Div Moffett Field CA 94035 USA

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems, operations, and infrastructure, to the system. To this end, historical data on airline scheduling and operations recovery are used to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, this paper provides a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

关键词： Neural Networks Unmanned Aircraft System Collaborative Decision Making Air Transportation distributed artificial intelligence False Positive Rate Airline Operations Gaussian Process Federal Aviation Administration Aircraft Operations

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RFID together with multi-agent systems to control global value chains

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ANNUAL REVIEWS IN CONTROL 2009年第2期33卷 185-195页

作者： Dias, J. C. Q. Calado, J. M. F. Osorio, A. Luis Morgado, L. F. Inst Super Engn Lisboa ISEL IDMEC P-1959007 Lisbon Portugal Univ Tecn Lisboa ISEL CENTEC Inst Super Tecn P-1049001 Lisbon Portugal Inst Super Engn Lisboa ISEL GIATSI P-1959007 Lisbon Portugal

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Complex systems Collaborative systems Cooperative systems Supply chain management Information and communication technologies Radio frequency identification distributed artificial intelligence Multi-agent systems Real-time decision-making

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Learning cases to resolve conflicts and improve group behavior

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 1998年第1期48卷 31-49页

作者： Haynes, T Sen, S Univ Tulsa Dept Math & Comp Sci Tulsa OK 74104 USA

Groups of agents following fixed behavioral rules can be limited in performance and efficiency. Adaptability and flexibility are key components of intelligent behavior which allow agent groups to improve performance in a given domain using prior problem-solving experience. We motivate the utility of individual learning by group members in the context of overall group behavior. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use a testbed problem from the distributed artificial intelligence literature to show that simultaneous learning by group members can lead to significant improvement in group performance and efficiency over agent groups following static behavioral rules. (C) 1998 Academic Press Limited.

关键词： distributed artificial intelligence Group Performance Agents MEMBERS Conflict (Psychology) Group Behavior Problem solving

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COOPERATIVE distributed PROBLEM-SOLVING FOR COMMUNICATION-NETWORK MANAGEMENT

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COMPUTER COMMUNICATIONS 1990年第9期13卷 547-557页

作者： WEIHMAYER, R BRANDAU, R GTE Laboratories Incorporated 40 Sylvan Road Waltham MA 02254 USA

Much of previous distributed artificial intelligence research has sought either to bring identical agents into closely coordinated groups, or to loosely coordinate the actions of dissimilar agents. The research described here explores close cooperation among heterogeneous agents, and is motivated by the requirements of a specific application in telecommunications network management: customer network control and joint private/public network management. In this domain, agents that manage the private and public networks must cooperate closely to provide satisfactory solutions to common network problems, yet they possess inherently distinct problem solving knowledge: private (or customer) networks are defined as logical networks constructed with the physical facilities provided by the public network. Thus, some of the network entities that define one agent's world knowledge are known by other agents at a different level of abstraction, creating a complex interdependence among agent problem solving activities. This paper provides some basic motivation for cooperative distributed problem solving and its application to communication network management in general, and reports on efforts to understand the nature of cooperation and the functionality of agents in the customer network control domain. In the process, the paper describes a three-agent facility failure problem and an associated interagent cooperation scenario, and presents a research testbed, TEAM-CPS, that explores cooperative problem solving and multiagent interaction.

关键词： communication network management cooperative problem solving distributed artificial intelligence multiagent interaction TEAM-CPS

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Cooperative multiagent systems: A personal view of the state of the art

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1999年第1期11卷 133-142页

作者： Lesser, VR Univ Massachusetts Dept Comp Sci Amherst MA 01003 USA

Scientific research and practice in multiagent systems focuses on constructing computational frameworks, principles, and models for how both small and large societies of intelligent, semiautonomous agents can interact effectively to achieve their goals. This article provides a personal view of the key application areas for cooperative multiagent systems, the major intellectual problems in building such systems, the underlying principles governing their design, and the major directions and challenges for future developments in this field.

关键词： multiagent systems coordination cooperation distributed problem solving distributed artificial intelligence computational organizations

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DNN Partitioning for Inference Throughput Acceleration at the Edge

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IEEE ACCESS 2023年 11卷 52236-52249页

作者： Feltin, Thomas Marcho, Leo Cordero-Fuertes, Juan-Antonio Brockners, Frank Clausen, Thomas H. Ecole Polytech F-91128 Palaiseau France Cisco Syst San Jose CA 95134 USA

Deep neural network (DNN) inference on streaming data requires computing resources to satisfy inference throughput requirements. However, latency and privacy sensitive deep learning applications cannot afford to offload computation to remote clouds because of the implied transmission cost and lack of trust in third-party cloud providers. Among solutions to increase performance while keeping computation on a constrained environment, hardware acceleration can be onerous, and model optimization requires extensive design efforts while hindering accuracy. DNN partitioning is a third complementary approach, and consists of distributing the inference workload over several available edge devices, taking into account the edge network properties and the DNN structure, with the objective of maximizing the inference throughput (number of inferences per second). This paper introduces a method to predict inference and transmission latencies for multi-threaded distributed DNN deployments, and defines an optimization process to maximize the inference throughput. A branch and bound solver is then presented and analyzed to quantify the achieved performance and complexity. This analysis has led to the definition of the acceleration region, which describes deterministic conditions on the DNN and network properties under which DNN partitioning is beneficial. Finally, experimental results confirm the simulations and show inference throughput improvements in sample edge deployments.

关键词： distributed artificial intelligence edge computing scheduling and task partitioning

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