Simulating social relations in multi-agent systems

Open distributed systems are comprised of a large number of heterogeneous nodes with disparate requirements and objectives, a number of which may not conform to the system specification. This thesis argues that activity in such systems can be regulated by using distributed mechanisms inspired by social science theories regarding similarity /kinship, trust, reputation, recommendation and economics. This makes it possible to create scalable and robust agent societies which can adapt to overcome structural impediments and provide inherent defence against malicious and incompetent action, without detriment to system functionality and performance. In particular this thesis describes: • an agent based simulation and animation platform (PreSage), which offers the agent developer and society designer a suite of powerful tools for creating, simulating and visualising agent societies from both a local and global perspective. • a social information dissemination system (SID) based on principles of self organisation which personalises recommendation and directs information dissemination. • a computational socio-cognitive and economic framework (CScEF) which integrates and extends socio-cognitive theories of trust, reputation and recommendation with basic economic theory. • results from two simulation studies investigating the performance of SID and the CScEF. The results show the production of a generic, reusable and scalable platform for developing and animating agent societies, and its contribution to the community as an open source tool. Secondly specific results, regarding the application of SID and CScEF, show that revealing outcomes of using socio-technical mechanisms to condition agent interactions can be demonstrated and identified by using Presage.Open Acces

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Human Factors in Agile Software Development

Through our four years experiments on students' Scrum based agile software development (ASD) process, we have gained deep understanding into the human factors of agile methodology. We designed an agile project management tool - the HASE collaboration development platform to support more than 400 students self-organized into 80 teams to practice ASD. In this thesis, Based on our experiments, simulations and analysis, we contributed a series of solutions and insights in this researches, including 1) a Goal Net based method to enhance goal and requirement management for ASD process, 2) a novel Simple Multi-Agent Real-Time (SMART) approach to enhance intelligent task allocation for ASD process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and morale management for ASD process, 4) the first large scale in-depth empirical insights on human factors in ASD process which have not yet been well studied by existing research, and 5) the first to identify ASD process as a human-computation system that exploit human efforts to perform tasks that computers are not good at solving. On the other hand, computers can assist human decision making in the ASD process.Comment: Book Draf

Evidentialist Foundationalist Argumentation in Multi-Agent Systems

This dissertation focuses on the explicit grounding of reasoning in evidence directly sensed from the physical world. Based on evidence from human problem solving and successes, this is a straightforward basis for reasoning: to solve problems in the physical world, the information required for solving them must also come from the physical world. What is less straightforward is how to structure the path from evidence to conclusions. Many approaches have been applied to evidence-based reasoning, including probabilistic graphical models and Dempster-Shafer theory. However, with some exceptions, these traditional approaches are often employed to establish confidence in a single binary conclusion, like whether or not there is a blizzard, rather than developing complex groups of scalar conclusions, like where a blizzard's center is, what area it covers, how strong it is, and what components it has. To form conclusions of the latter kind, we employ and further develop the approach of Computational Argumentation. Specifically, this dissertation develops a novel approach to evidence-based argumentation called Evidentialist Foundationalist Argumentation (EFA). The method is a formal instantiation of the well-established Argumentation Service Platform with Integrated Components (ASPIC) framework. There are two primary approaches to Computational Argumentation. One approach is structured argumentation where arguments are structured with premises, inference rules, conclusions, and arguments based on the conclusions of other arguments, creating a tree-like structure. The other approach is abstract argumentation where arguments interact at a higher level through an attack relation. ASPIC unifies the two approaches. EFA instantiates ASPIC specifically for the purpose of reasoning about physical evidence in the form of sensor data. By restricting ASPIC specifically to sensor data, special philosophical and computational advantages are gained. Specifically, all premises in the system (evidence) can be treated as firmly grounded axioms and all arguments' conclusions can be numerically calculated directly from their premises. EFA could be used as the basis for well-justified, transparent reasoning in many domains including engineering, law, business, medicine, politics, and education. To test its utility as a basis for Computational Argumentation, we apply EFA to a Multi-Agent System working in the problem domain of Sensor Webs on the specific problem of Decentralized Sensor Fusion. In the Multi-Agent Decentralized Sensor Fusion problem, groups of individual agents are assigned to sensor stations that are distributed across a geographical area, forming a Sensor Web. The goal of the system is to strategically share sensor readings between agents to increase the accuracy of each individual agent's model of the geophysical sensing situation. For example, if there is a severe storm, a goal may be for each agent to have an accurate model of the storm's heading, severity, and focal points of activity. Also, since the agents are controlling a Sensor Web, another goal is to use communication judiciously so as to use power efficiently. To meet these goals, we design a Multi-Agent System called Investigative Argumentation-based Negotiating Agents (IANA). Agents in IANA use EFA as the basis for establishing arguments to model geophysical situations. Upon gathering evidence in the form of sensor readings, the agents form evidence-based arguments using EFA. The agents systematically compare the conclusions of their arguments to other agents. If the agents sufficiently agree on the geophysical situation, they end communication. If they disagree, then they share the evidence for their conclusions, consuming communication resources with the goal of increasing accuracy. They execute this interaction using a Share on Disagreement (SoD) protocol. IANA is evaluated against two other Multi-Agent System approaches on the basis of accuracy and communication costs, using historical real-world weather data. The first approach is all-to-all communication, called the Complete Data Sharing (CDS) approach. In this system, agents share all observations, maximizing accuracy but at a high communication cost. The second approach is based on Kalman Filtering of conclusions and is called the Conclusion Negotiation Only (CNO) approach. In this system, agents do not share any observations, and instead try to infer the geophysical state based only on each other's conclusions. This approach saves communication costs but sacrifices accuracy. The results of these experiments have been statistically analyzed using omega-squared effect sizes produced by ANOVA with p-values < 0.05. The IANA system was found to outperform the CDS system for message cost with high effect sizes. The CDS system outperformed the IANA system for accuracy with only small effect sizes. The IANA system was found to outperform the CNO system for accuracy with mostly high and medium effect sizes. The CNO system outperformed the IANA system for message costs with only small effect sizes. Given these results, the IANA system is preferable for most of the testing scenarios for the problem solved in this dissertation

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)

Opening up the interpretation process in an open learner model

Opening a model of the learner is a potentially complex operation. There are many aspects of the learner that can be modelled, and many of these aspects may need to be opened in different ways. In addition, there may be complicated interactions between these aspects which raise questions both about the accuracy of the underlying model and the methods for representing a holistic view of the model. There can also be complex processes involved in inferring the learner's state, and opening up views onto these processes - which leads to the issues that are the main focus of this paper: namely, how can we open up the process of interpreting the learner's behaviour in such a manner that the learner can both understand the process and challenge the interpretation in a meaningful manner. The paper provides a description of the design and implementation of an open learner model (termed the xOLM) which features an approach to breaking free from the limitations of "black box" interpretation. This approach is based on a Toulmin-like argumentation structure together with a form of data fusion based on an adaptation of Dempster-Shafer. However, the approach is not without its problems. The paper ends with a discussion of the possible ways in which open learner models might open up the interpretation process even more effectively