Multi Agent Systems for Artificial Life Domain:Using Multi Agent system for modeling and simulation of the anticipation behavior and its application in real life domain Ahmed Elmahalawy
This book constitutes the revised and selected papers from the 6th International Workshop on Engineering Multi-Agent Systems held in Stockholm, Sweden, in July 2018, in conjunction with AAMAS 2018. The 17 full papers presented in this volume were carefully reviewed and selected from 32 submissions. The book also contains a state-of-the-art paper that reflects on the role and potential of MAS engineering in a number of key facets. The papers are clustered around the following themes: programming agents and MAS, agent-oriented software engineering, formal analysis techniques, rational agents, modeling and simulation, frameworks and application domains.
During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. However, users do not usually trust complete automation during decision-making for certain domains with natural variation, like biology; they need a way to be able to control and/or intervene with the system´s reasoning to verify parts of the process. This book provides a thorough analysis of the problem and presents a data-centric methodology of measuring decision criticality and describe its potential use. We argue that agent technology is a natural fit for the design of distributed heterogeneous integration systems, particularly in bioinformatics, and we propose a multi-agent system design and architecture as the basis of our framework.
Multi-agent systems are claimed to be especially suited to the development of software systems that are decentralized, can deal flexibly with dynamic conditions, and are open to system components that come and go. This is why they are used in domains such as manufacturing control, automated vehicles, and e-commerce markets. Danny Weyns´ book is organized according to the postulate that ´´developing multi-agent systems is 95% software engineering and 5% multi-agent systems theory.´´ He presents a software engineering approach for multi-agent systems that is heavily based on software architecture - with, for example, tailored patterns such as ´´situated agent´´, ´´virtual environment´´, and ´´selective perception´´ - and on middleware for distributed coordination - with programming abstractions such as ´´views´´ and ´´roles.´´ Next he shows the feasibility and applicability of this approach with the development of an automated transportation system consisting of a number of automatic guided vehicles transporting loads in an industrial setting. Weyns puts the development of multi-agent systems into a larger perspective with traditional software engineering approaches. With this, he opens up opportunities to exploit the body of knowledge developed in the multi-agent systems community to tackle some of the difficult challenges of modern-day software systems, such as decentralized control, location-awareness, self-adaption, and large-scale. Thus his book is of interest for both researchers and industrial software engineers who develop applications in areas such as distributed control systems and mobile applications where such requirements are of crucial importance.