Abstract: This paper proposes a multi-agent approach to power system restoration. The proposed system consists of a number of bus agents (BAGS) and a single facilitator agent (FAG). BAG is developed to decide a suboptimal target configuration after a fault occurrence by interacting with other BAGS based on only locally available information, while FAG is to act as a manager in the decision process. The interaction of several simple agents leads to a dynamic system, allowing efficient approximation of a solution. Simulation results have demonstrated that this method is able to reach suboptimal target configurations, which are favorably compared with those obtained by a mathematical programming approach.

Abstract: Recent peer-to-peer (P2P) systems are characterized by decentralized control, large scale and extreme dynamism of their operating environment. As such, they can be seen as instances of complex adaptive systems (CAS) typically found in biological and social sciences. We describe Anthill, a framework to support the design, implementation and evaluation of P2P applications based on ideas such as multi-agent and evolutionary programming borrowed from CAS. An Anthill system consists of a dynamic network of peer nodes; societies of adaptive agents travel through this network, interacting with nodes and cooperating with other agents in order to solve complex problems. Anthill can be used to construct different classes of P2P services that exhibit resilience, adaptation and self-organization properties. We also describe preliminary experiences with Anthill in implementing a file sharing application.

Abstract: The problem of calculating a degree of reputation for agents acting as assistants to the members of an electronic community is discussed and a solution presented. Usual reputation mechanisms rely on feedback after interaction between agents. An alternative way to establish reputation is related with the position of each member of a community within the corresponding social network. We propose a method based on this idea, which is also used by well-known ranking algorithms, discuss its properties as well as experimental results and compare them to other reputation mechanisms for electronic communities supported by agents. The method proposed uses only local information in order to extract reputation and it is able to adapt automatically to the topology of the network or graph.

Abstract: Adaptive software is used in situations where either the environment is unpredictable or the system is open. This paper presents a methodology named ADELFE, which is led by the Rational Unified Process (RUP) but is devoted to software engineering of adaptive multi-agent systems. ADELFE guarantees that the software is developed according to the AMAS theory1. We focus this presentation on the additions of ADELFE regarding the three first core workflows of the RUP. Therefore, during the requirements phase, the environment of the studied system must be defined and characterized. Then, in the analysis phase, the engineer is guided to decide to use adaptive multi-agent technology and to identify the agents through the system and the environment models. Finally, the design workflow of ADELFE must provide the cooperative agent?s model and helps the developer to define the local agents? behavior. We illustrate the methodology by applying it to a case study: a timetable design.

Abstract: In the last few years, there has been increasing interest from the agent community in the use of techniques from decision theory and game theory Our aims in this article are firstly to briefly summarize the key concepts of decision theory and game theory, secondly to discuss how these tools are being applied in agent systems research, and finally to introduce this special issue of Autonomous Agents and Multi-Agent Systems by reviewing the papers that appear

Abstract: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.

Abstract: An apparatus and method for iterative problem solving that uses intelligent agents such as a brain agent, a profile agent, a personality agent, a knowledge agent and an error handling agent to interpret questions posed by a user and to provide responses back to the user. The apparatus and method may further use a mood agent, a visual agent, sound agent, a tactile agent, and a smell/taste agent and various connectors to external data sources to interpret questions and provide responses back to the user. The apparatus and method may further parse questions in a conceptual manner. The apparatus and method may further optimize its system performance by evolving with and reacting to user interactions.

Abstract: This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

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Abstract: Since advanced traveler information systems (ATIS) have been introduced, their potential benefits as well as their drawbacks have been discussed controversially. This will continue as long as the drivers’ reactions upon current or even predictive information about the traffic situation are not known. Thus, traffic models that also consider this feedback are necessary. In this paper, we address a basic two-route scenario with different types of information and study the impact of it using simulations. The road users are modeled as agents, a natural and promising approach to describe them. Different ways of generating current information are tested. It is pointed out that the nature of the information very much influences the potential benefits of the ATIS.

Abstract: This paper reports our experiences with agent-based architectures for intelligent traffic management systems. We describe and compare integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona. Both systems draw upon traffic management agents that use similar knowledge-based reasoning techniques in order to deal with local traffic problems. Still, the former achieves agent coordination based on a traditional centralized mechanism, while in the latter coordination emerges upon the lateral interaction of autonomous traffic management agents. We evaluate the potentials and drawbacks of both multiagent architectures for the domain, and develop some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.

Abstract: Agent-based simulations are models where multiple entities sense and stochastically respond to conditions in their local environments, mimicking complex large-scale system behavior. We provide an overview of some important issues in the modeling and analysis of agent-based systems. Examples are drawn from a range of fields: biological modeling, sociological modeling, and industrial applications, though we focus on recent results for a variety of military applications. Based on our experiences with various agent-based models, we describe issues that simulation analysts should be aware of when embarking on agent-based model development. We also describe a number of tools (both graphical and analytical) that we have found particularly useful for analyzing these types of simulation models. We conclude with a discussion of areas in need of further investigation.

Abstract: The agent paradigm is currently attracting much research. A mobile agent is a particular type of agent with the ability to migrate from one host to another, where it can resume its execution. We consider security issues that need to be addressed before multi-agent systems in general, and mobile agents in particular, can be a viable solution for a broad range of commercial applications. This is done by considering the implications of the characteristics given to agents and the general properties of open multi-agent systems. The paper then looks in some more detail at security technology and methods applicable to mobile agent systems.

Abstract: Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.

Abstract: Perhaps the most difficult challenge in understanding social phenomena is their intractably complex nature. For much of the 20th century social scientists attempted to unravel the complexities of the social realm by emulating the methodologies of the natural sciences. Although these approaches enhanced social science research, they have fallen short of capturing emergent behavior and self-organization. For some years now, new approaches to the study of complex adaptive systems have offered researchers in both the physical and social sciences an important new theoretical and methodological framework for helping to understand a variety of nonlinear, dynamic systems. Complex adaptive systems are characterized often by “agents” interacting or capable of interacting with each other in dynamic, often nonlinear and surprising ways. Most social phenomena would readily fit the description of a complex adaptive system. The difficulty researchers have faced, given the opaque character of social processes, is to develop methodologies appropriate for better exploring such complex adaptive systems. A growing number of social scientists, dissatisfied with traditional methodologies, are seeking new methods for exploring the complexities of social dynamics. One of the emerging developments is the use of agent-based modeling and simulation to examine how social phenomena …

Abstract: A compositional method is presented for the verification of multi-agent systems. The advantages of the method are the well-structuredness of the proofs and the reusability of parts of these proofs in relation to reuse of components. The method is illustrated for an example multi-agent system, consisting of co-operative information gathering agents. This application of the verification method results in a formal analysis of pro-activeness and reactiveness of agents, and shows which combinations of pro-activeness and reactiveness in a specific type of information agents lead to a successful cooperation.

Abstract: Existing modeling frameworks for manufacturing system control can be classified into hierarchical, heterarchical, and hybrid control frameworks. The main drawbacks of existing frameworks are discussed in this paper. A new hybrid modeling framework is also described. It is a hybrid of the two: hierarchical and heterarchical frameworks. In this proposed framework, entities (e.g., parts) and resources (e.g., material handling devices, machines, cells, departments) are modeled as holonic structures that use intelligent agents to function in a cooperative manner so as to accomplish individual, as well as cell-wide and system-wide objectives. To overcome the structural rigidity and lack of flexibility, negotiation mechanisms for real-time task allocation are used. Lower-level holons may autonomously make their negotiations within the boundary conditions that the higher-level holons set. Horizontal, as well as vertical decisions, are made between various levels of controllers, and these are explicitly captured in the model.

Abstract: This paper proposes a generic approach or protocol engineering through the analysis,the specification,and the verification of such protocols when several agents are involved. This approach is three folds:1)Starting from semi-formal specification by means o Protocol Diagrams (AUML),both formal specification of interaction protocols and their verification are allowed thanks to Colored Petri Nets (CPN);2) Debugging and qualitative analysis o interactions are based on distributed observation associated with the true concurrency semantics (i.e.CPN unfolding)and ;3)CPN formalism is extended to Recursive CPN (RCPN)with abstraction in order to deal with open protocols.The main interest of abstraction is the design of fexible protocols giving agents more autonomy during interaction.In addition,abstraction allows concise modeling and easier verification. measures,performance measures .

Abstract: We provide a theoretical framework that consists of graph theoretical and Lyapunov-based approaches to stability analysis and distributed control of multi-agent formations. This framework relays on the notion of graph rigidity as a means of identifying the shape variables of a formation. Using this approach, we can formally define formations of multiple vehicles and three types of stabilization/tracking problems for dynamic multi-agent systems. We show how these three problems can be addressed mutually independent of each other for a formation of two agents. Then, we introduce a procedure called dynamic node augmentation that allows construction of a larger formation with more agents that can be rendered structurally stable in a distributed manner from some initial formation that is structurally stable. We provide two examples of formations that can be controlled using this approach, namely, the V-formation and the diamond formation.

Abstract: Target detection and tracking is one of the most important and fundamental technologies to develop real-world computer vision systems such as security and traffic monitoring systems. This paper first categorizes target tracking systems based on characteristics of scenes, tasks, and system architectures. Then we present a real-time cooperative multitarget tracking system. The system consists of a group of active vision agents (AVAs), where an AVA is a logical model of a network-connected computer with an active camera. All AVAs cooperatively track their target objects by dynamically exchanging object information with each other With this cooperative tracking capability, the system as a whole can track multiple moving objects persistently even under complicated dynamic environments in the real world. In this paper we address the technologies employed in the system and demonstrate their effectiveness.

Abstract: We propose that through the formalization of concepts related to trust, a more accurate model of trust can be implemented. This paper presents a new model of trust that is based on the formalization of reputation. A multidisciplinary approach is taken to understanding the nature of trust and its relation to reputation. Through this approach, a practical definition of reputation is adopted from sociological contexts and a model of reputation is designed and presented. Reputation is defined as role fulfillment. To formalize reputation, it is necessary to formalize the expectations placed upon an agent within a particular multi-agent system (MAS). In this case, the agents are part of an informationsharing society. Five roles are defined along with the ways in which these roles are objectively fulfilled. Through the measurement of role fulfillment, a vector representing reputation can be developed. This vector embodies the magnitude of the reputation and describes the patterns of behavior associated with the direction of the vector. Experiments are conducted to verify the sensibility of the proposed models for role fulfillment and overall reputation. The simulation results show that the roles, defined for building reputation in an information-sharing MAS environment, react to different agent and user actions in a manner consistent with the formal definitions.

Abstract: The Internet provides a large-scale environment for (intelligent) software agents. Agents are autonomous (mobile) processes, capable of communication with other agents, interaction with the world, and adaptation to changes in their environment. Current approaches to support agents are not geared for large-scale settings. The near future holds thousands of agents, hosts, messages, and migratory movements of agents. These large-scale aspects require a new approach to facilitate the development of agent applications and support. AgentScape is a scalable agent-based distributed system, described in this paper, that aims at tackling these aspects.

Abstract: In this paper, we present an abstract formal model of decision-making in a social setting that covers all aspects of the process, from recognition of a potential for cooperation t hrough to joint decision. In a multi-agent environment, where self-motivated autonomous agents try to pursue their own goals, a joint decision cannot be taken for granted. In order to decide effectively, agents need the ability to (a) represent and maintain a model of their own mental attitudes, (b) reason about other agents’ mental attitudes, and (c) influence other agents’ mental states. Social mental shaping is advocated as a general mechanism for attempting to have an impact on agents’ mental states in order to increase their cooperativeness towards a joint decision. Our approach is to specify a novel, high-level architecture for collaborative decision-making in which the mentalistic notions of belief, desire, goal, intention, preference and commitment play a central role in guiding the individual agent’s and the group’s decision-making behaviour. We identify preconditions that must be fulfilled before colla borative decision-making can commence and prescribe how cooperating agents should behave, in terms of their own decision-making apparatus and their interactions with others, when the decision-making process is progressing satisfactorily. The model is formalized through a new, many-sorted, multi-modal logic.

Abstract: Ontologies play important role in knowledge sharing and exploration, particularly in communication in multi-agent systems. We briefly survey, compare and analyze current usage of ontologies in the area of manufacturing and compare it with the ontology modelling for the Semantic Web. We focus on the problem of scalable interoperability in open heterogeneous multi-agent systems, such as supply chains. A study of transformation of a particular ontology from the manufacturing domain into the form suitable for communication with Semantic Web agents is presented. We conclude with a discussion of what we see as the next important steps in the development of ontologies in the manufacturing domain in order to have more automated approaches for ontological knowledge integration.

Abstract: Analytical techniques are generally inadequate for dealing with causal interrelationships among a set of individual and social concepts. Usually, causal maps are used to cope with this type of interrelationships. However, the classical view of causal maps is based on an intuitive view with ad hoc rules and no precise semantics of the primitive concepts, nor a sound formal treatment of relations between concepts. We solve this problem by proposing a formal model for causal maps with a precise semantics based on relational algebra and the software tool, CM-RELVIEW, in which it has been implemented. Then, we investigate the issue of using this tool in multiagent environments by explaining through different examples how and why this tool is useful for the following aspects: 1) the reasoning on agents' subjective views, 2) the qualitative distributed decision making, and 3) the organization of agents considered as a holistic approach. For each of these aspects, we focus on the computational mechanisms developed within CM-RELVIEW to support it.

Abstract: Agent-based computing represents an exciting new synthesis for both Artificial Intelligence and, more generally, Computer Science. It has the potential to improve the theory and the practice of modelling, designing and implementing complex computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. To rectify this situation, this paper aims to tackle exactly this issue. The standpoint of this analysis is the role of agent-based software in solving complex, realworld problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level interactions, and that can operate within flexible organisational structures.

Abstract: The Cognitive Agents Specification Language (CASL) is a frame-work for specifying multiagent systems. It has a mix of declarative and procedural components to facilitate the specification and verification of complex multiagent systems. In this paper, we describe CASL and a verification environment (CASLve) for it based on the PVS verification system. We give an example of a multiagent meeting scheduler application specified with CASL. To illustrate the verification system, we discuss a proof we carried out in it, namely, that all bounded-loop CASL specifications terminate.

Abstract: The RoboFlag competition was proposed by the second author as a means to study cooperative control of multi-vehicle systems. Here we study a simplified version of the competition that we model as a hybrid system. We use an optimization approach to synthesize control policies by solving mixed integer linear programs.