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Because of the clearly important role cooperative systems play in areas such as military sciences, biology, communications, robotics, and economics, just to name a few, the study of cooperative systems has intensified. This book provides an insight in the basic understanding of cooperative systems as well as in theory, modeling, and applications of cooperative control, optimization and related problems.
Team formation is the most rudimentary form of interactions in distributed AI and multiagent systems as it allows coherent collections of agents to work together in a beneficial manner towards a common goal of interest. Basically, individual expertise are assembled together in an additive fashion for accomplishing tasks together. A plethora of the related studies found in the literature often make several unrealistic assumptions such as coordination amongst the agents, or agents having knowledge of the whole environment, or agents and/or tasks are of the same kind, or a static environment setting. Against this background, we argue that there are real-world characteristics that make team formation more challenging: (1) There is no or minimal pre-coordination since storage and retrieval is a costly affair, (2) There is diversity amongst types of agents (Apprentices, Generalists, and Specialists) and tasks (Low, Medium, and High), (3) The environment is open i.e., agents and tasks can leave and enter the environment, and (4) Agents are continuously learning and improving their capabilities. The main contribution of this research is to study in great depths the impacts of various permutations of open and diverse environments on team formation and how agents learn to form these teams. Based on the findings of these studies, we demonstrate that both diversity and openness have impacts on the team formation. Having evaluated the results of the impacts of openness and diversity on the environment we, to strengthen the robustness of the original model, we introduce an enhanced version of this model. The next contribution of this thesis is putting forth an enhanced probabilistic modelling solution. To be able to carry out new investigations and introduce the new model, we have restructured and cleaned up the simulation software used for building the original model. Having implemented the enhanced model, we then show how this new model performs better than the original model. The final contribution of this thesis was to show why the new model performed better than the original model.
Developments in Intelligent Agent Technologies and Multi-Agent Systems: Concepts and Applications discusses research on emerging technologies and systems based on agent and multi-agent paradigms across various fields of science, engineering and technology. This book is a collection of work that covers conceptual frameworks, case studies, and analysis while serving as a medium of communication among researchers from academia, industry and government.
What makes teamwork tick? Cooperation matters, in daily life and in complex applications. After all, many tasks need more than a single agent to be effectively performed. Therefore, teamwork rules! Teams are social groups of agents dedicated to the fulfilment of particular persistent tasks. In modern multiagent environments, heterogeneous teams often consist of autonomous software agents, various types of robots and human beings. Teamwork in Multi-agent Systems: A Formal Approach explains teamwork rules in terms of agents' attitudes and their complex interplay. It provides the first comprehensive logical theory, TeamLog, underpinning teamwork in dynamic environments. The authors justify design choices by showing TeamLog in action. The book guides the reader through a fascinating discussion of issues essential for teamwork to be successful: What is teamwork, and how can a logical view of it help in designing teams of agents? What is the role of agents' awareness in an uncertain, dynamic environment? How does collective intention constitute a team? How are plan-based collective commitments related to team action? How can one tune collective commitment to the team's organizational structure and its communication abilities? What are the methodological underpinnings for teamwork in a dynamic environment? How does a team and its attitudes adjust to changing circumstances? How do collective intentions and collective commitments arise through dialogue? What is the computational complexity of TeamLog? How can one make TeamLog efficient in applications? This book is an invaluable resource for researchers and graduate students in computer science and artificial intelligence as well as for developers of multi-agent systems. Students and researchers in organizational science, in particular those investigating teamwork, will also find this book insightful. Since the authors made an effort to introduce TeamLog as a conceptual model of teamwork, understanding most of the book requires solely a basic logical background.
ANEMONA is a multi-agent system (MAS) methodology for holonic manufacturing system (HMS) analysis and design. ANEMONA defines a mixed top-down and bottom-up development process, and provides HMS-specific guidelines to help designers identify and implement holons. The analysis phase is defined in two stages: System Requirements Analysis, and Holon Identification and Specification. This analysis provides high-level HMS specifications, adopting a top-down recursive approach which provides a set of elementary elements and assembling rules. The next stage is Holon Design, a bottom-up process to produce the system architecture from the analysis models. The Holons Implementation stage produces an Executable Code for the SetUp and Configuration stage. Finally, maintenances functions are executed in the Operation and Maintenance stage. The book will be of interest to researchers and students involved in artificial intelligence and software engineering, and manufacturing engineers in industry and academia.
A selective review of modern decision science and implications for decision-support systems. The study suggests ways to synthesize lessons from research on heuristics and biases with those from "naturalistic research." It also discusses modern tools, such as increasingly realistic simulations, multiresolution modeling, and exploratory analysis, which can assist decisionmakers in choosing strategies that are flexible, adaptive, and robust.