Locating Agents in RFID Architectures

The use of software agents can create an “intelligent” interface between users’ preferences and the back‐end systems. Agents are now able to interact and communicate with each other, forming a virtual community and feeding back the user with suggestions. Innovative systems related to Asset Tracking, Inventory and Shelving architectures are more often involving advanced communication techniques (e.g., RFID); these systems are responsible for user authentication and objects verification. RFID systems could have jamming situations where many objects are moving at the same time and in the same direction. Moreover, other disadvantages have also been observed, such as hindering further implementations, privacy and security issues problems, in addition to the system’s disruptive behavior in case of crowd checkouts (e.g., Supermarket and Airports). Addressing these disadvantages, this paper proposes a possible integration between a Multi‐Agent framework and an RFID‐based application (back‐end). This integration would allow objects (such as passports or goods) with RFID tags to better check‐out through airports or supermarket gates that contain RFID‐readers

A multi-agent approach to the deregulation and restructuring of power industry

In recent years, the electric utility industry throughout the world has been facing pressure to deregulate or restructure in order to increase its efficiency, to reduce operational costs or to give consumers more alternatives. The once centralized system planning and operation management must be remodelled to adapt to the new market environment. Subject to unavoidable constraints such as the capacity of generation stations, physical limitations of the transmission lines, and demand on days-ahead scheduling, the current trading mechanism needs to be revised so that any party can be involved in this free-market environment. The paper presents a multi-agent approach to resolve the multilateral trading problem. The authors have implemented a prototype based on bilateral Shapley value and Internet technologies. The prototype has been tested with a classical six-bus system.published_or_final_versio

On the convergence of autonomous agent communities

This is the post-print version of the final published paper that is available from the link below. Copyright @ 2010 IOS Press and the authors.Community is a common phenomenon in natural ecosystems, human societies as well as artificial multi-agent systems such as those in web and Internet based applications. In many self-organizing systems, communities are formed evolutionarily in a decentralized way through agents' autonomous behavior. This paper systematically investigates the properties of a variety of the self-organizing agent community systems by a formal qualitative approach and a quantitative experimental approach. The qualitative formal study by applying formal specification in SLABS and Scenario Calculus has proven that mature and optimal communities always form and become stable when agents behave based on the collective knowledge of the communities, whereas community formation does not always reach maturity and optimality if agents behave solely based on individual knowledge, and the communities are not always stable even if such a formation is achieved. The quantitative experimental study by simulation has shown that the convergence time of agent communities depends on several parameters of the system in certain complicated patterns, including the number of agents, the number of community organizers, the number of knowledge categories, and the size of the knowledge in each category

Anytime Coalition Structure Generation with Worst Case Guarantees

Coalition formation is a key topic in multiagent systems. One would prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show necessary for establishing a bound. We present an algorithm that establishes a tight bound within this minimal amount of search, and show that any other algorithm would have to search strictly more. The fraction of nodes needed to be searched approaches zero as the number of agents grows. If additional time remains, our anytime algorithm searches further, and establishes a progressively lower tight bound. Surprisingly, just searching one more node drops the bound in half. As desired, our algorithm lowers the bound rapidly early on, and exhibits diminishing returns to computation. It also drastically outperforms its obvious contenders. Finally, we show how to distribute the desired search across self-interested manipulative agents

Dynamic Multi-Agent Based Variety Formation and Steering in Mass Customization

Large product variety in mass customization involves a high internal complexity level inside a company’s operations, as well as a high external complexity level from a customer’s perspective. To cope with both complexity problems, an information system based on agent technology is able to be identified as a suitable solution approach. The mass customized products are assumed to be based on a modular architecture and each module variant is associated with an autonomous rational agent. Agents have to compete with each other in order to join coalitions representing salable product variants which suit real customers’ requirements. The negotiation process is based on a market mechanism supported by the target costing concept and a Dutch auction. Furthermore, in order to integrate the multi-agent system in the existing information system landscape of the mass customizer, a technical architecture is proposed and a scenario depicting the main communication steps is specified.Product Configuration, Mass Customization, Variety Formation and Steering, Multi Agent System

An attitude based modeling of agents in coalition

One of the main underpinning of the multi-agent systems community is how and why autonomous agents should cooperate with one another. Several formal and computational models of cooperative work or coalition are currently developed and used within multi-agent systems research. The coalition facilitates the achievement of cooperation among different agents. In this paper, a mental construct called attitude is proposed and its significance in coalition formation in a dynamic fire world is discussed. This paper presents ABCAS (Attitude Based Coalition Agent System) that shows coalitions in multi-agent systems are an effective way of dealing with the complexity of fire world. It shows that coalitions explore the attitudes and behaviors that help agents to achieve goals that cannot be achieved alone or to maximize net group utility

Game theoretical multi-agent modelling of coalition formation for multilateral trades

In recent years, electric utility industries worldwide have been undergoing deregulation to introduce competitiveness in the generation, transmission and distribution of electric power. The once centralised system planning and operation management must be remodelled to adapt to the new market structure. In particular, the trading mechanism needs to be totally revised as any party may now get involved in this free-market, subject to the unavoidable constraints such as generation volumes, physical transmission means and days-ahead scheduling. This paper presents a multi-agent model in conjunction with game theory to resolve the coalition formation for multilateral trades. The authors have implemented the model using the Java language and the JATLite/JAT0.3 agent development tools developed by Stanford University.published_or_final_versio

Group Formation Among Peer-to-Peer Agents: Learning Group Characteristics

This paper examines the decentralized formation of groups within a peer-to-peer multi-agent system. More specifically, it frames group formation as a clustering problem, and examines how to determine cluster characteristics such as area and density in the absence of information about the entire data set, such as the number of points, the number of clusters, or the maximum distance between points, that are available to centralized clustering algorithms. We develop a method in which agents individually search for other agents with similar characteristics in a peer-to-peer manner. These agents group into small centrally controlled clusters which learn cluster parameters by examining and improving their internal composition over time. We show through simulation that this method allows us to find clusters of a wide variety of sizes without adjusting agent parameters