Showing papers on "Multi-agent system published in 2004"

Trust in multi-agent systems

Abstract: Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

Developing Intelligent Agent Systems: A Practical Guide

Abstract: Build your own intelligent agent system. Intelligent agent technology is a tool of modern computer science that can be used to engineer complex computer programmes that behave rationally in dynamic and changing environments. Applications range from small programmes that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This powerful technology is not widely used, however, as developing intelligent agent software requires high levels of training and skill. The authors of this book have developed and tested a methodology and tools for developing intelligent agent systems. With this methodology (Prometheus) developers can start agent-oriented designs and implementations easily from scratch saving valuable time and resources. Developing Intelligent Agent Systems not only answers the questions 'what are agents?' and 'why are they useful?' but also the crucial question: 'how do I design and build intelligent agent systems?' The book covers everything a practitioner needs to know to begin to effectively use this technology - including an introduction to the notion of agents, a description of the concepts involved, and a software engineering methodology.

Solving Distributed Constraint Optimization Problems Using Cooperative Mediation

Abstract: Distributed Constraint Optimization Problems (DCDP) have, for a long time, been considered an important research area for multi-agent systems because a vast number of real-world situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve them efficiently using fully distributed algorithms which are often based on existing centralized techniques. In this paper, we present an optimal, distributed algorithm called optimal asynchronous partial overlay (OptAPO) for solving DCOPs that is based on a partial centralization technique called cooperative mediation. The key ideas used by this algorithm are that agents, when acting as a mediator, centralize relevant portions of the DCDP, that these centralized subproblems overlap, and that agents increase the size of their subproblems as the problem solving unfolds. We present empirical evidence that shows that OptAPO performs better than other known, optimal DCOP techniques.

Toward autonomic web services trust and selection

Abstract: Emerging Web services standards enable the development of large-scale applications in open environments. In particular, they enable services to be dynamically bound. However, current techniques fail to address the critical problem of selecting the right service instances. Service selection should be determined based on user preferences and business policies, and consider the trustworthiness of service instances.We propose a multiagent approach that naturally provides a solution to the selection problem. This approach is based on an architecture and programming model in which agents represent applications and services. The agents support considerations of semantics and quality of service (QoS). They interact and share information, in essence creating an ecosystem of collaborative service providers and consumers. Consequently, our approach enables applications to be dynamically configured at runtime in a manner that continually adapts to the preferences of the participants. Our agents are designed using decision theory and use ontologies. We evaluate our approach through simulation experiments.

An artificial intelligence perspective on autonomic computing policies

Abstract: We introduce a unified framework that interrelates three different types of policies that will be used in autonomic computing system: action, goal, and utility function policies. Our policy framework is based on concepts from artificial intelligence such at: states, actions, and rational agents. We show how the framework can be used to support the use of all three types of policies within a single autonomic component or system, and use the framework to discuss the relative merits of each type.

Intelligent agents meet the semantic Web in smart spaces

Abstract: A new smart meeting room system called EasyMeeting explores the use of multi-agent systems, Semantic Web ontologies, reasoning, and declarative policies for security and privacy. Building on an earlier pervasive computing system, EasyMeeting provides relevant services and information to meeting participants based on their situational needs. The system also exploits the context-aware support provided by the Context Broker Architecture (Cobra). Cobra's intelligent broker agent maintains a shared context model for all computing entities in the space and enforces user-defined privacy policies.

Challenges and Research Directions in Agent-Oriented Software Engineering

Abstract: Agent-based computing is a promising approach for developing applications in complex domains. However, despite the great deal of research in the area, a number of challenges still need to be faced (i) to make agent-based computing a widely accepted paradigm in software engineering practice, and (ii) to turn agent-oriented software abstractions into practical tools for facing the complexity of modern application areas. In this paper, after a short introduction to the key concepts of agent-based computing (as they pertain to software engineering), we characterise the emerging key issues in multiagent systems (MASs) engineering. In particular, we show that such issues can be analysed in terms of three different “scales of observation”, i.e., in analogy with the scales of observation of physical phenomena, in terms of micro, macro, and meso scales. Based on this characterisation, we discuss, for each scale of observation, what are the peculiar engineering issues arising, the key research challenges to be solved, and the most promising research directions to be explored in the future.

A Multi-Agent Systems Approach to Autonomic Computing

Abstract: The goal of autonomic computing is to create computing systems capable of managing themselves to a far greater extent than they do today. This paper presents Unity, a decentralized architecture for autonomic computing based on multiple interacting agents called autonomic elements. We illustrate how the Unity architecture realizes a number of desired autonomic system behaviors including goal-driven self-assembly, self-healing, and real-time self-optimization. We then present a realistic prototype implementation, showing how a collection of Unity elements self-assembles, recovers from certain classes of faults, and manages the use of computational resources (e.g. servers) in a dynamic multi-application environment. In Unity, an autonomic element within each application environment computes a resource-level utility function based on information specified in that applicationýs service-level utility function. Resource-level utility functions from multiple application environments are sent to a Resource Arbiter element, which computes a globally optimal allocation of servers across the applications. We present illustrative empirical data showing the behavior of our implemented system in handling realistic Web-based transactional workloads running on a Linux cluster.

Coordination Artifacts: Environment-Based Coordination for Intelligent Agents

Abstract: Direct interaction and explicit communication are not always the best approaches for achieving coherent systemic behaviour in the context of Multi-Agent Systems (MAS). This is evident when taking into account recent approaches dealing with environment-based coordination such as stigmergy and, more generally, mediated interaction. In this paper we propose a conceptual, formal and engineering framework based on the notion of coordination artifact, which aims at generally systematising implicit communication and environment-based coordination for heterogeneous, possibly intelligent agents. The features and benefits of our approach are exemplified in the Follow-me situation, where an agentýs action/plan is considered as a model for the action/plan of other agents. We model this class of problems in terms of coordination artifacts, from simple to more challenging cases, stressing the advantages with respect to more "standard" MAS approaches.

Power systems stability control: reinforcement learning framework

Abstract: In this paper, we explore how a computational approach to learning from interactions, called reinforcement learning (RL), can be applied to control power systems. We describe some challenges in power system control and discuss how some of those challenges could be met by using these RL methods. The difficulties associated with their application to control power systems are described and discussed as well as strategies that can be adopted to overcome them. Two reinforcement learning modes are considered: the online mode in which the interaction occurs with the real power system and the offline mode in which the interaction occurs with a simulation model of the real power system. We present two case studies made on a four-machine power system model. The first one concerns the design by means of RL algorithms used in offline mode of a dynamic brake controller. The second concerns RL methods used in online mode when applied to control a thyristor controlled series capacitor (TCSC) aimed to damp power system oscillations.

The design of a multi-agent transformer condition monitoring system

Abstract: Online diagnostics and online condition monitoring are important functions within the operation and maintenance of power transformers. This paper describes how a multi-agent system (MAS) for transformer condition monitoring has been designed to employ the data generated by the ultra high frequency (UHF) monitoring of partial discharge activity. It describes the rationale behind the use of multi-agent techniques, and the problems overcome through this technology. Every aspect of the MAS design is discussed. In addition, the design and performance of the intelligent interpretation techniques are detailed.

FIRE: an integrated trust and reputation model for open multi-agent systems

Abstract: Trust and reputation are central to effective interactions in open multi-agent systems in which agents, that are owned by a variety of stakeholders, can enter and leave the system at any time. This openness means existing trust and reputation models cannot readily be used. To this end, we present FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent's likely performance. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide a trust metric in virtually all circumstances. FIRE is empirically benchmarked and is shown to help agents effectively select appropriate interaction partners.

Real-world market representation with agents

Abstract: As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial and regulatory environments in which they operate. A new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation (ABMS) approach. The purpose of an ABMS model is not necessarily to predict the outcome of a system but to reveal and understand the complex and aggregate system behaviors that emerge from the interactions of the heterogeneous individual entities. Emergent behavior is a key feature of ABMS and is not easily inferred from the simple sum of the behavior of its components. By relying on both established engineering modeling techniques as well as advanced quantitative economic market principles, the ABMS approach is uniquely suited to addressing the strategic issues of interest to different market participants as well as those of market monitors and regulators.

Multi-agent systems in production planning and control: an overview

Abstract: The ever fast changes of customers’ needs and demands ask for reconfigurable and adaptive production systems, which can provide companies with the proper level of agility and effectiveness, without disregarding at the same time cost factors. In the last decade, a large amount of research works on the adoption of multi-agent systems (MAS) in several industrial environments has flourished. This approach, unlike traditional centralized or multilevel hierarchical approaches, assumes the presence of several decision-making entities, distributed inside the manufacturing system, interacting and cooperating each other in order to achieve optimal global performance. Aim of this paper is at first to provide readers, which are not experienced with the multi-agent approach, with some definitions and categorizations of this paradigm. Secondarily, by making use of an extensive database of more than 100 contributions on this field, authors intend to evaluate how multi-agents systems have really impacted on the industria...

A multiagent system for microgrids

Abstract: This paper presents the capabilities offered by multiagent system technology in the operation of a microgrid. A microgrid is a new type of power system, which is formed by the interconnection of small, modular generation to low voltage distribution systems. Microgrids can be connected to the main power network or be operated autonomously, similar to power systems of physical islands. The use of MAS technology can solve a number of specific operational problems: the small DG (distributed generation) units have different owners, so centralized control is difficult. Several decisions should be taken locally. Lack of dedicated communication facilities. Microgrids will operate in a liberalized market so the decisions of the controller of each unit concerning the market should have a certain degree of "intelligence". The local DG units besides selling power to the network have also other tasks: producing heat for local installations, keeping the voltage locally at a certain level or providing a backup system for local critical loads in case of a failure of the main system. These tasks reveal the importance of the distributed control and autonomous operation.

Holons and agents

Abstract: The future of the manufacturing sector in the world will be determined by how it meets the challenges of the 21st century Two paradigms promise to meet these challenges, multi-agent systems (MAS) and holonic system (HS) Currently, there is some misunderstanding about the relationships between these two approaches The aim of this work is to make a study of all the characteristics of the HS and the MAS approach, in order to illustrate a comprehensive comparison of holons and agents, and, at the same time, to present a survey about the main research works in both areas

Cougaar: a scalable, distributed multi-agent architecture

Abstract: Distributed multi-agent systems have become more mature in recent years, with the growing potential to handle large volumes of data and coordinate the operations of many organizations. However, widespread adoption by industry and government has been blocked in part by concerns about scalability and survivability, especially in unpredictable environments of attacks and system failures. In this paper, we present Cougaar, an open-source Java-based agent architecture that provides a survivable base on which to deploy large-scale, robust distributed applications. We define the challenging problem of the UltraLog project; a distributed logistics application comprised of more than 1000 agents distributed over 100 hosts, which guided the design of the Cougaar architecture to ensure scalability, robustness, and security. We conclude with a survey of Cougaar uses as the preferred agent platform for a variety of applications.

Consensus of information under dynamically changing interaction topologies

Abstract: This paper considers the problem of information consensus among multiple agents in the presence of limited and unreliable information exchange with dynamically changing interaction topologies. Both discrete and continuous update schemes are proposed for information consensus. The paper shows that information consensus under dynamically changing interaction topologies can be achieved asymptotically if the union of the directed interaction graphs across some time intervals has a spanning tree frequently enough as the system evolves. Simulation results show the effectiveness of our update schemes.

Generating Coalition Structures with Finite Bound from the Optimal Guarantees

Abstract: The coalition formation process, in which a number of independent, autonomous agents come together to act as a collective, is an important form of interaction in multiagent systems. When effective, such coalitions can improve the performance of the individual agents and/or of the system as a whole. However, one of the main problems that hinders the wide spread adoption of coalition formation technologies is the computational complexity of coalition structure generation. That is, once a group of agents has been identified, how can it be partitioned in order tomaximise the social payoff? This problem has been shown to be NP-hard and even finding a sub-optimal solution requires searching an exponential number of solutions. Against this background, this paper reports on a novel anytime algorithm for coalition structure generation that produces solutions that are within a finite bound from the optimal. Our algorithm is benchmarked against Sandholm et al.ýs algorithm [8] (the only other known algorithm for this task that can also establish a worst-case bound from the optimal) and is shown to be up to 10^379 times faster (for systems containing 1000 agents) when small bounds from the optimal are desirable.

Reasoning about Commitments in the Event Calculus: An Approach for Specifying and Executing Protocols

Abstract: Commitments among agents are widely recognized as an important basis for organizing interactions in multiagent systems. We develop an approach for formally representing and reasoning about commitments in the event calculus. We apply and evaluate this approach in the context of protocols, which represent the interactions allowed among communicating agents. Protocols are essential in applications such as electronic commerce where it is necessary to constrain the behaviors of autonomous agents. Traditional approaches, which model protocols merely in terms of action sequences, limit the flexibility of the agents in executing the protocols. By contrast, by formally representing commitments, we can specify the content of the protocols through the agents' commitments to one another. In representing commitments in the event calculus, we formalize commitment operations and domain-independent reasoning rules as axioms to capture the evolution of commitments. We also provide a means to specify protocol-specific axioms through the agents' actions. These axioms enable agents to reason about their actions explicitly to flexibly accommodate the exceptions and opportunities that may arise at run time. This reasoning is implemented using an event calculus planner that helps determine flexible execution paths that respect the given protocol specifications.

An Extended Multi-Agent Negotiation Protocol

Abstract: This article presents a task allocation protocol that is efficient in time and tolerates crash failures in multi-agent systems. The protocol is an extension of the negotiation protocol defined by Smith and Davis [25, 26] for task allocation. Our extension of the Contract Net Protocol (1) enables an agent to manage several negotiation processes in parallel; (2) optimizes the length of the negotiation processes among agents; (3) reduces the contractors' decommitment situations; (4) enables the detection of failures of an agent participating in a negotiation process and prevents a negotiation process with blocked agents.

Developing an integrated trust and reputation model for open multi-agent systems

Abstract: Trust and reputation are central to effective interactions in open multi-agent systems in which agents, that are owned by a variety of stakeholders, can enter and leave the system at any time. This openness means existing trust and reputation models cannot readily be used. To this end, we present FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide a trust metric in most circumstances. FIRE is empirically benchmarked and is shown to help agents effectively select appropriate interaction partners.

Devising A Trust Model For Multi-Agent Interactions Using Confidence And Reputation

Abstract: In open environments in which autonomous agents can break contracts, computational models of trust have an important role to play in determining who to interact with and how interactions unfold To this end, we develop such a trust model, based on confidence and reputation, and show how it can be concretely applied, using fuzzy sets, to guide agents in evaluating past interactions and in establishing new contracts with one another

Sparse cooperative Q-learning

Abstract: Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents. We first examine a compact representation in which the agents need to explicitly coordinate their actions only in a predefined set of states. Next, we use a coordination-graph approach in which we represent the Q-values by value rules that specify the coordination dependencies of the agents at particular states. We show how Q-learning can be efficiently applied to learn a coordinated policy for the agents in the above framework. We demonstrate the proposed method on the predator-prey domain, and we compare it with other related multiagent Q-learning methods.

Trust in Multiagent Systems

Abstract: Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

Resource Allocation in the Grid Using Reinforcement Learning

Abstract: In this paper we study a minimalist decentralized algorithm for resource allocation in a simplified Grid-like environment. We consider a system consisting of large number of heterogenous reinforcement learning agents that share common resources for their computational needs. There is no communication between the agents: the only information that agents receive is the (expected) completion time of a job it submitted to a particular resource and which serves as a reinforcement signal for the agent. The results of our experiments suggest that reinforcement learning can be used to improve the quality of resource allocation in large scale heterogenous system.