This paper investigates the emergence of retail clusters on supply chains comprised of suppliers, retailers, and consumers. Agent-based models are employed to study retail location choice in a market of homogeneous goods and a market of complementary goods. On a circle comprised of discrete locales, retailers play a non-cooperative game by choosing locales to maximize profits which are impacted by their distance to consumers and to suppliers. The authors findings disclose that in a market of homogeneous products symmetric distributions of retail clusters rise out of competition between individual retailers; average cluster density and cluster size change dynamically as retailers enter the market. In a market of two complementary goods, multiple equilibria of retail distributions are found to be common; a single cluster of retailers has the highest probability to emerge. Overall, the authors results show that retail clusters emerge from the balance between retailers' proximity to their customers, their competitors, their complements, and their suppliers.

Why Retailers Cluster: An Agent Model of Location Choice on Supply Chains

Agent action diagram: Toward a model for emergency management system

Department of Electrical Engineering and Information Technology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, Gheorghe Marinescu 38, Targu Mureș, 540139, Romania Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35131, Greece Department of Mathematics and Informatics, Vasile Alecsandri University of Bacau, Calea Marasesti 157, Bacau, 600115, Romania

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.

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Application of Artificial Capital Market in Task Allocation in Multi-robot Foraging

We propose a novel trust metric for social networks which is suitable for application in recommender systems. It is personalised and dynamic and allows to compute the indirect trust between two agents which are not neighbours based on the direct trust between agents that are neighbours. In analogy to some personalised versions of PageRank, this metric makes use of the concept of feedback centrality and overcomes some of the limitations of other trust this http URL particular, it does not neglect cycles and other patterns characterising social networks, as some other algorithms do. In order to apply the metric to recommender systems, we propose a way to make trust dynamic over time. We show by means of analytical approximations and computer simulations that the metric has the desired properties. Finally, we carry out an empirical validation on a dataset crawled from an Internet community and compare the performance of a recommender system using our metric to one using collaborative filtering.

Personalised and Dynamic Trust in Social Networks

This paper presents a novel approach towards showing how contractor in agent-based simulation for complex warfare system such as multi-sensor battlefield reconnaissance system can be selected in Contract Net Protocol (CNP) with high efficiency. We first analyze agent and agent-based simulation framework, CNP and collaborators, and present agents interaction chain used to actualize CNP and establish agents trust network. We then obtain contractor's importance weight and dynamic trust by presenting fuzzy similarity-based algorithm and trust modifying algorithm, thus we propose contractor selecting approach based on maximum dynamic integrative trust. We validate the feasibility and capability of this approach by implementing simulation, analyzing compared results and checking the model.

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Zhiming Dong

Papers

A Novel Approach to Selecting Contractor in Agent-based Multi-sensor Battlefield Reconnaissance Simulation