Abstract: "This book lays out a vision for a coherent framework for understanding complex systems'' (from the foreword by J. Doyne Farmer). By developing the genuine idea of Brownian agents, the author combines concepts from informatics, such as multiagent systems, with approaches of statistical many-particle physics. This way, an efficient method for computer simulations of complex systems is developed which is also accessible to analytical investigations and quantitative predictions. The book demonstrates that Brownian agent models can be successfully applied in many different contexts, ranging from physicochemical pattern formation, to active motion and swarming in biological systems, to self-assembling of networks, evolutionary optimization, urban growth, economic agglomeration and even social systems.

Abstract: We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.

Abstract: Decentralized control of a cooperative multi-agent system is the problem faced by multiple decision-makers that share a common set of objectives. The decision-makers may be robots placed at separate geographical locations or computational processes distributed in an information space. It may be impossible or undesirable for these decision-makers to share all their knowledge all the time. Furthermore, exchanging information may incur a cost associated with the required bandwidth or with the risk of revealing it to competing agents. Assuming that communication may not be reliable adds another dimension of complexity to the problem.This paper develops a decision-theoretic solution to this problem, treating both standard actions and communication as explicit choices that the decision maker must consider. The goal is to derive both action policies and communication policies that together optimize a global value function. We present an analytical model to evaluate the trade-off between the cost of communication and the value of the information received. Finally, to address the complexity of this hard optimization problem, we develop a practical approximation technique based on myopic meta-level control of communication.

Abstract: Recent multi-agent extensions of Q-Learning require knowledge of other agents' payoffs and Q-functions, and assume game-theoretic play at all times by all other agents. This paper proposes a fundamentally different approach, dubbed "Hyper-Q" Learning, in which values of mixed strategies rather than base actions are learned, and in which other agents' strategies are estimated from observed actions via Bayesian inference. Hyper-Q may be effective against many different types of adaptive agents, even if they are persistently dynamic. Against certain broad categories of adaptation, it is argued that Hyper-Q may converge to exact optimal time-varying policies. In tests using Rock-Paper-Scissors, Hyper-Q learns to significantly exploit an Infinitesimal Gradient Ascent (IGA) player, as well as a Policy Hill Climber (PHC) player. Preliminary analysis of Hyper-Q against itself is also presented.

Abstract: Invited Talks.- Making Agents Acceptable to People.- Coalition Formation: Towards Feasible Solutions Abstract of a Key-Note Speech.- Coalition Task Support Using I-X and ?I-N-C-A?.- Formal Methods.- Towards Motivation-Based Decisions for Worth Goals.- Modal Structure for Agents Interaction Based on Concurrent Actions.- A Multi-agent Modal Language for Concurrency with Non-communicating Agents.- Self-Synchronization of Cooperative Agents in a Distributed Environment.- MIP-Nets: A Compositional Model of Multiagent Interaction.- Social Knowledge & Meta-Reasoning.- Calibrating Collective Commitments.- Abstract Architecture for Meta-reasoning in Multi-agent Systems.- Balancing Individual Capabilities and Social Peer Pressure for Role Adoption.- From Social Agents to Multi-agent Systems: Preliminary Report.- Negotiation & Policies.- DAML-Based Policy Enforcement for Semantic Data Transformation and Filtering in Multi-agent Systems.- Architectures for Negotiating Agents.- RIO : Roles, Interactions and Organizations.- Conversation Mining in Multi-agent Systems.- Ontologies & Languages.- The Knowledge Market: Agent-Mediated Knowledge Sharing.- Ontology of Cooperating Agents by Means of Knowledge Components.- Mapping between Ontologies in Agent Communication.- A Social ACL Semantics by Deontic Constraints.- A Formal Specification Language for Agent Conversations.- Planning.- Framework for Multi-agent Planning Based on Hybrid Automata.- Multi-agent System for Resource Allocation and Scheduling.- Towards Autonomous Decision Making in Multi-agent Environments Using Fuzzy Logic.- Towards an Object Oriented Implementation of Belief-Goal-Role Multi-agent Systems.- Coalitions.- Fuzzy Coalition Formation among Rational Cooperative Agents.- Multi-agent Simulation of Work Teams.- Multi-agent Knowledge Logistics System "KSNet": Implementation and Case Study for Coalition Operations.- Evolution & Emergent Behavior.- Learning User Preferences for Multi-attribute Negotiation: An Evolutionary Approach.- A Model of Co-evolution in Multi-agent System.- Emergence of Specialized Behavior in a Pursuit-Evasion Game.- On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor.- Forgiveness in Strategies in Noisy Multi-agent Environments.- Platforms.- An Unified Framework for Programming Autonomous, Intelligent and Mobile Agents.- Tailoring an Agent Architecture to a Flexible Platform Suitable for Cooperative Robotics.- Airports for Agents: An Open MAS Infrastructure for Mobile Agents.- Beyond Prototyping in the Factory of Agents.- Agent Oriented Software Engineering with INGENIAS.- Protocols.- Requirement Analysis for Interaction Protocols.- Engineering a Protocol Server Using Strategy-Agents.- Refinement of Open Protocols for Modelling and Analysis of Complex Interactions in Multi-agent Systems.- Security.- Biological Approach to System Information Security (BASIS): A Multi-agent Approach to Information Security.- Adaptive Agents Applied to Intrusion Detection.- Communication Security in Multi-agent Systems.- Teamwork of Hackers-Agents: Modeling and Simulation of Coordinated Distributed Attacks on Computer Networks.- Real-Time & Synchronization.- Formal Modeling of Dynamic Environments for Real-Time Agents.- Deliberative Server for Real-Time Agents.- Regional Synchronization for Simultaneous Actions in Situated Multi-agent Systems.- A Multi-agent System for Dynamic Network Reconfiguration.- Industrial Applications.- A Highly Distributed Intelligent Multi-agent Architecture for Industrial Automation.- The Cambridge Packing Cell - A Holonic Enterprise Demonstrator.- Towards Autonomy, Self-Organisation and Learning in Holonic Manufacturing.- An Agent-Based Personalized Producer/Consumer Scenario.- Application of Intelligent Agents in Power Industry: Promises and Complex Issues.- E-business & Virtual Enterprises.- Brokering in Electronic Insurance Markets.- Modelling Electronic Organizations.- The Use of Adaptive Negotiation by a Shopping Agent in Agent-Mediated Electronic Commerce.- Agent Interaction Protocols for the Selection of Partners for Virtual Enterprises.- Web & Mobile Agents.- A Multiagent-Based Peer-to-Peer Network in Java for Distributed Spam Filtering.- Engineering Web Service Invocations from Agent Systems.- A Component Based Multi-agent Architecture to Support Mobile Business Processes.- Code Complexity Metrics for Mobile Agents Implemented with Aspect/J(TM).

Abstract: This article reviews recent progress made by computational studies investigating the emergence, via learning or evolutionary mechanisms, of communication among a collection of agents. This work spans issues related to animal communication and the origins and evolution of language. The studies reviewed show how population size, spatial constraints on agent interactions, and the tasks involved can all influence the nature of the communication systems and the ease with which they are learned and/or evolved. Although progress in this area has been substantial, we are able to identify some important areas for future research in the evolution of language, including the need for further computational investigation of key aspects of language such as open vocabulary and the more complex aspects of syntax.

Abstract: Increasingly, many systems are being conceptualized, designed, and implemented as marketplaces in which autonomous software entities (agents) trade services. These services can be commodities in e-commerce applications or data and knowledge services in information economies. In many of these cases, there are both multiple agents that are looking to procure services and multiple agents that are looking to sell services at any one time. Such marketplaces are termed continuous double auctions (CDAs). Against this background, this paper develops new algorithms that buyer and seller agents can use to participate in CDAs. These algorithms employ heuristic fuzzy rules and fuzzy reasoning mechanisms in order to determine the best bid to make given the state of the marketplace. Moreover, we show how an agent can dynamically adjust its bidding behavior to respond effectively to changes in the supply and demand in the marketplace. We then show, by empirical evaluations, how our agents outperform four of the most prominent algorithms previously developed for CDAs (several of which have been shown to outperform human bidders in experimental studies).

Abstract: There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual ‘agents’ and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a ‘bottom-up’ approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other h...

Abstract: In this paper, we present an approach for the design of complex adaptive systems, based on adaptive multi-agent systems and emergence. We expound the AMAS theory (Adaptive Multi-Agent Systems) and its technical working. This theory gives local agent design criteria so as to enable the emergence of an organization within the system and thus, of the global function of the system. We also present the theorem of functional adequacy witch ensures that a cooperative self organizing system performs a suitable work. Applications of this theory in the multi-agent system framework led us to define the architecture and a general algorithm for cooperative agents. The originality of our approach lies in the very generic manner our re-organization rules work and that they are completely independent from the function the system has to compute.

Abstract: With the growing usage of the world-wide ICT networks, agent technologies and multiagent systems are attracting more and more attention, as they perform well in environments that are not necessarily well-structured and benevolent. Looking at the problem solving capacity of multiagent systems, emergent system behaviour is one of the most interesting phenomena, however, there is more to multiagent systems design than the interaction between a number of agents: For an effective system behaviour we need structure and organisation. But the organisation of a multiagent systems is difficult to specify at design time in the face of a changing environment.

Abstract: Although well understood in the single-agent framework, the use of traditional reinforcement learning (RL) algorithms in multi-agent systems (MAS) is not always justified. The feedback an agent experiences in a MAS, is usually influenced by the other agents present in the system. Multi agent environments are therefore non-stationary and convergence and optimality guarantees of RL algorithms are lost. To better understand the dynamics of traditional RL algorithms we analyze the learning process in terms of evolutionary dynamics. More specifically we show how the Replicator Dynamics (RD) can be used as a model for Q-learning in games. The dynamical equations of Q-learning are derived and illustrated by some well chosen experiments. Both reveal an interesting connection between the exploitation-exploration scheme from RL and the selection-mutation mechanisms from evolutionary game theory.

Abstract: Supply chain formation is the process of determining the structure and terms of exchange relationships to enable a multilevel, multiagent production activity. We present a simple model of supply chains, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. To decentralize the formation process, we introduce a market price system over the resources produced along the chain. In a competitive equilibrium for this system, agents choose locally optimal allocations with respect to prices, and outcomes are optimal overall. To determine prices, we define a market protocol based on distributed, progressive auctions, and myopic, non-strategic agent bidding policies. In the presence of resource contention, this protocol produces better solutions than the greedy protocols common in the artificial intelligence and multiagent systems literature. The protocol often converges to high-value supply chains, and when competitive equilibria exist, typically to approximate competitive equilibria. However, complementarities in agent production technologies can cause the protocol to wastefully allocate inputs to agents that do not produce their outputs. A subsequent decommitment phase recovers a significant fraction of the lost surplus.

Abstract: Protection engineers use data from a range of monitoring devices to perform post-fault disturbance diagnosis. In the past, heterogeneous intelligent systems have been developed to interpret the data and provide information to engineers to assist with the disturbance diagnosis task. The majority of these systems remain standalone due to the problems associated with systems integration. This paper proposes the use of multiagent systems for providing a flexible and scalable alternative to existing integration approaches. A novel multiagent system (MAS) has been developed entitled protection engineering diagnostic agents (PEDAs) which integrates a legacy SCADA interpretation system with new systems for digital fault recorder (DFR) record interpretation and for enhancing fault record retrieval from remote DFRs. The use of MAS technology provides a flexible and scalable architecture open to the introduction of new data interpretation systems. The paper discusses the benefits of a multiagent approach and the design and implementation of PEDA.

Abstract: With the growing usage of the world-wide ICT networks, agent technologies and multiagent systems are attracting more and more attention, as they perform well in environments that are not necessarily well-structured and benevolent. Looking at the problem solving capacity of multiagent systems, emergent system behaviour is one of the most interesting phenomena, however, there is more to multiagent systems design than the interaction between a number of agents: For an effective system behaviour we need structure and organisation. But the organisation of a multiagent systems is difficult to specify at design time in the face of a changing environment. This paper presents basic concepts for a theory of holonic multiagent systems to both provide a methodology for the recursive modelling of agent groups, and allow for dynamic reorganisation during runtime.

Abstract: We present a distributed approach to self-organization in a distributed sensor network. The agents in the system use a series of negotiations incrementally to form appropriate coalitions of sensor and processing resources.Since the system is cooperative, we have developed a range of protocols that allow the agents to share meta-level information before they allocate resources. On one extreme the protocols are based on local utility computations, where each agent negotiates based on its local perspective. From there, a continuum of additional protocols exists in which agents base decisions on marginal social utility, the combination of an agent's marginal utility and that of others. We present a formal framework that allows us to quantify how social an agent can be in terms of the set of agents that are considered and how the choice of a certain level affects the decisions made by the agents and the global utility of the organization.Our results show that by implementing social agents, we obtain an organization with a high global utility both when agents negotiate over complex contracts and when they negotiate over simple ones. The main difference between the two cases is mainly the rate of convergence. Our algorithm is incremental, and therefore the organization that evolves can adapt and stabilize as agents enter and leave the system.

Abstract: Traditional’ urban simulation models have a number of weaknesses that limit their suitability as planning support tools. However, a new wave of models is currently under development in academic circles, and it is beginning to find application in practical contexts. Based around two simulation techniques that have origins in artificial life and artificial intelligence — cellular automata and multi-agent systems — it offers great potential for planning support tools, with the capacity to simulate individual households and units of the built environment in a truly dynamic, realistic and highly flexible manner. This chapter presents an overview of traditional land use and transport models as planning support tools and examines theirfragilities before reviewing a new wave of urban models. Additionally, it considers the challenges facing the use of new techniques in operational models.

Abstract: Agent technology is one of the most interesting developments in the field of distributed artificial intelligence. It has a wide range of applications, with information management, intelligent user interfaces, personal assistants, and Internet commerce among the most popular. This article defines a generic architecture that applies the multiagent systems methodology to the field of substation automation, describes the design of a system to be implemented based on this architecture, and proposes several possible applications. Compared with SCADA or client-server substation automation solutions, an agent-based system offers a number of advantages. Each function or task of the system, such as the management of a single IED, can be encapsulated within a separate agent, making the system highly modular. Agents are loosely coupled, typically communicating via messaging rather than by procedure calls (remote or local), and, using directory services, new functions can easily be added to an agent-based system by creating a new agent, which will then make its capabilities available to others. The inherently distributed power system architecture is suited ideally to a multiagent system, which provides greater autonomy to each of the constituent parts than a traditional system.

Abstract: In this article, an agent-based architecture in which cooperation is regulated by contracts is proposed as a flexible approach to dynamic shop floor re-engineering. It describes the dynamic and flexible cooperation of manufacturing agents, representing manufacturing resources and how they can be created from a generic agent template. Agents' behaviour and contract types and structure are discussed. The first experimental results are also introduced.

Abstract: This paper examines a method of clustering within a fully decentralized multi-agent system. Our goal is to group agents with similar objectives or data, as is done in traditional clustering. However, we add the additional constraint that agents must remain in place on a network, instead of first being collected into a centralized database. To do this we connect agents in a random network and have them search in a peer-to-peer fashion for other similar agents. We thus aim to tackle the basic clustering problem on an Internet scale and create a method by which agents themselves can be grouped, forming coalitions. In order to investigate the feasibility of a decentralized approach, this paper presents a number of simulation experiments involving agents representing two-dimensional points. A comparison between our method's clustering ability and that of the k-means clustering algorithm is presented. Generated data sets containing 2,500 to 160,000 points (agents) grouped in 25 to 1,600 clusters are examined. Results show that our decentralized agent method produces a better clustering than the centralized k-means algorithm, quickly placing 95% to 99% of points correctly. The the time required to find a clustering depends on the quality of solution required; a fairly good solution is quickly converged on, and then slowly improved. Overall, our experiments indicate that the time to find a particular quality of solution increases less than linearly with the number of agents.

Abstract: Simulations are used extensively for studying artificial intelligence. However, the simulation technology in use by and designed for the artificial intelligence community often fails to take advantage of much of the work by the larger simulation community to produce distributed, repeatable, and efficient simulations. We present the system for parallel agent discrete event simulation, (SPADES), which is a simulation environment for the artificial intelligence community. SPADES focuses on the agent as a fundamental simulation component. The thinking time of an agent is tracked and reflected in the results of the agents' actions by using a software-in-the-loop mechanism. SPADES supports distributed execution of the agents across multiple systems, while at the same time producing repeatable results regardless of network or system load. We discuss the design of SPADES and give experimental results. SPADES is flexible enough for a variety of application domains in the artificial intelligence research community.

Abstract: In this work, we suggest representing multiagent systems using computational models, choosing, specifically, Multi-Prover Interactive Protocols to represent agent systems and the interactions occurring within them. This approach enables us to analyze complexity issues related to multiagent systems. We focus here on the complexity of coordination and study the possible sources of this complexity. We show that there are complexity bounds that cannot be lowered even when approximation techniques are applied.

Abstract: One of the main consequences of the extended enterprise is the emergence of new forms of relationships between the customer and the supplier in order to ensure the quality of service of the object throughout the life cycle Innovative communication and co-operation methods are needed to support these new relationships The combination of modern information processing and communication tools, commonly referred to as tele-service, offers the technical support required to access remote information Indeed, it is easier to transfer information and knowledge to different actors than to move an actor to the site However, even if this technical support is necessary for information communication, it is insufficient to develop a co-operation-based working situation that involves many self-motivated customers and suppliers sharing a common goal This synergy is an emergent property of the system as a whole, and it is not expected to be obtained as a simple sum of its components For example, a challenging problem in the field of product manufacturing is to assist the operator in its decision-making, when the system functioning is degraded, to preserve the system under service at maximum (anticipation of the failure) while remaining it in a space of allowed operation (to avoid consequences of an error in judgement) That means an evolution from tele-service to e-work and e-maintenance in particular where the assistance to operator results from collaboration of maintenance processes and experts Consequently, this paper examines some issues and challenges involved in the evolution from the tele-maintenance of an industrial platform to the e-maintenance, and then proposes a multi-agent system-based collaboration as a solution to implement the e-maintenance experiments

Abstract: Agent-based modeling and simulation (ABMS) is a relatively new development that has found extensive use in areas such as social sciences, economics, biology, ecology etc. Can ABMS be effectively used in finding answers to complex construction systems? Our focus is to provide some answers to this question. Initial experimentation is conducted to understand the advantages of using ABMS either in isolation or in combination with traditional simulation methodologies. We provide a summary of this experimentation, conclusions and sets the agenda for future research in this area.

Abstract: Security plays an important role in the development of multiagent systems. However, a careful analysis of software development processes shows that the definition of security requirements is, usually, considered after the design of the system. This is, mainly, due to the fact that agent oriented software engineering methodologies have not integrated security concerns throughout their developing stages. The integration of security concerns during the whole range of the development stages could help towards the development of more secure multiagent systems. In this paper we introduce extensions to the Tropos methodology to enable it to model security concerns throughout the whole development process. A description of the new concepts is given along with an explanation of how these concepts are integrated to the current stages of Tropos. An example from the health care sector is used to illustrate the above.