An intelligent peer-to-peer multi-agent system for collaborative management of bibliographic databases

This paper describes the design of a peer-to-peer system for collaborative management of distributed bibliographical databases. The goal of this system is twofold: firstly, it aims at providing help for users to manage their local bibliographical databases. Secondly, it offers the possibility to exchange bibliographical data among like-minded user groups in an implicit and intelligent manner. Each user is assisted by a personal agent that provides help such as: filling in bibliographical records, verifying the correctness of information entered and more importantly, recommendation of relevant bibliographical references. To do this, the personal agent needs to collaborate with its peers in order to get relevant recommendations. Each agent applies a case-based reasoning approach in order to provide peers with requested recommendations. The paper focuses mainly on describing the recommendation computation approach

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Peer-to-Peer Bartering: Swapping Amongst Self-interested Agents

Large--scale distributed environments can be seen as a conflict between the selfish aims of the participants and the group welfare of the population as a whole. In order to regulate the behavior of the participants it is often necessary to introduce mechanisms that provide incentives and stimulate cooperative behavior in order to mitigate for the resultant potentially undesirable availability outcomes which could arise from individual actions.The history of economics contains a wide variety of incentive patterns for cooperation. In this thesis, we adopt bartering incentive pattern as an attractive foundation for a simple and robust form of exchange to re-allocate resources. While bartering is arguably the world's oldest form of trade, there are still many instances where it surprises us. The success and survivability of the barter mechanisms adds to its attractiveness as a model to study.In this thesis we have derived three relevant scenarios where a bartering approach is applied. Starting from a common model of bartering: - We show the price to be paid for dealing with selfish agents in a bartering environment, as well as the impact on performance parameters such as topology and disclosed information.- We show how agents, by means of bartering, can achieve gains in goods without altruistic agents needing to be present.- We apply a bartering--based approach to a real application, the directory services.The core of this research is the analysis of bartering in the Internet Age. In previous times, usually economies dominated by bartering have suffered from high transaction costs (i.e. the improbability of the wants, needs that cause a transaction occurring at the same time and place). Nowadays, the world has a global system of interconnected computer networks called Internet. This interconnected world has the ability to overcome many challenges of the previous times. This thesis analysis the oldest system of trade within the context of this new paradigm. In this thesis we aim is to show thatbartering has a great potential, but there are many challenges that can affect the realistic application of bartering that should be studied.The purpose of this thesis has been to investigate resource allocation using bartering mechanism, with particular emphasis on applications in largescale distributed systems without the presence of altruistic participants in the environment.Throughout the research presented in this thesis we have contributed evidence that supports the leitmotif that best summarizes our work: investigation interactions amongst selfish, rational, and autonomous agents with incomplete information, each seeking to maximize its expected utility by means of bartering. We concentrate on three scenarios: one theoretical, a case of use, and finally a real application. All of these scenarios are used for evaluating bartering. Each scenario starts from a common origin, but each of them have their own unique features.The final conclusion is that bartering is still relevant in the modern world

Theoretical and Computational Basis for CATNETS - Annual Report Year 2

In this work the self-organising potential of the CATNETS allocation mechanism is described to provide a more comprehensive view on the research done in this project. The formal description of either the centralised and decentralised approach is presented. Furthermore the agents' bidding model is described and a comprehensive overview on how the catallactic mechanism is incorporated into the middleware and simulator environments is given. --Decentralized Market Mechanisms,Centralized Market Mechanisms,Catallaxy,Market Engineering,Simulator Integration,Prototype Integration

Theoretical and Computational Basis for CATNETS - Annual Report Year 3

In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined. --Grid Computing

Social learning in models and minds

After more than a century in which social learning was blackboxed by evolutionary biologists, psychologists and economists, there is now a thriving industry in cognitive neuroscience producing computational models of learning from and about other agents. This is a hugely positive development. The tools of computational cognitive neuroscience are rigorous and precise. They have the potential to prise open the black box. However, we argue that, from the perspective of a scientific realist, these tools are not yet being applied in an optimal way. To fulfil their potential, the shiny new methods of cognitive neuroscience need to be better coordinated with old-fashioned, contrastive experimental designs. Inferences from model complexity to cognitive complexity, of the kind made by those who favour lean interpretations of behaviour (‘associationists’), require social learning to be tested in challenging task environments. Inferences from cognitive complexity to social specificity, made by those who favour rich interpretations (‘mentalists’), call for non-social control experiments. A parsimonious model that fits current data is a good start, but carefully designed experiments are needed to distinguish models that tell us how social learning could be done from those that tell us how it is really done