Multi-agent systems is a subfield of Distributed Artificial Intelligence that has experienced rapid growth because of the flexibility and the intelligence available solve distributed problems. In this chapter, a brief survey of multi-agent systems has been presented. These encompass different attributes such as architecture, communication, coordination strategies, decision making and learning abilities. The goal of this chapter is to provide a quick reference to assist in the design of multi-agent systems and to highlight the merit and demerits of the existing methods.

An Introduction to Multi-Agent Systems

The anticipated increase in popular use of the Internet will create more opportunities in distance learning, electronic commerce, and multimedia communication, but it will also create more challenges in organizing information and facilitating its efficient retrieval. From the network perspective, there will be additional challenges and problems in meeting bandwidth requirements and network management. Many researchers believed that the mobile agent paradigm (mobile object) could propose several attractive solutions to deal with such challenges and problems. A number of mobile agent systems have been designed and implemented in academic institutions and commercial firms. However, few applications were found to take advantage of the mobile agent. Among the hurdles facing this emerging paradigm are concerns about security requirements and efficient resource management. This article introduces the core concepts of this emerging paradigm, and presents an account of current research efforts in the context of telecommunications. The goal is to provide the interested reader with a clear background of the opportunities and challenges this emerging paradigm brings about, and a descriptive look at some of the forerunners that are providing experimental technologies supporting this paradigm.

/pdf/mobile-software-agents-an-overview-1mie2vb8a7.pdf

Mobile software agents: an overview

The specification and management of quality of service (QoS) is important in networks and distributed computing systems, particularly to support multimedia applications. The advent of portable laptop computers, palmtops, and personal digital assistants with integrated communication capabilities facilitates mobile computing. This article is a survey of QoS concepts and techniques for mobile distributed computing environments. The requirements of current and future mobile computing are examined and the services required to support mobility are discussed. Generic concepts of QoS specification and management are overviewed followed by an analysis of the QoS work specific to mobile computing environments.

https://spiral.imperial.ac.uk:8443/bitstream/10044/1/6381/1/IEEE-QoS_survey.pdf

A survey of quality of service in mobile computing environments

A system and method of detecting an aberrant message is provided. An ordered set of words within the message is detected. The set of words found within the message is linked to a corresponding set of expected words, the set of expected words having semantic attributes. A set of grammatical structures represented in the message is detected, based on the ordered set of words and the semantic attributes of the corresponding set of expected words. A cognitive noise vector comprising a quantitative measure of a deviation between grammatical structures represented in the message and an expected measure of grammatical structures for a message of the type is then determined. The cognitive noise vector may be processed by higher levels of the neural network and/or an external processor.

Intelligent control with hierarchical stacked neural networks

Software agent technology will have a significant impact on the shape of the global information society in the next millennium. This branch of systems engineering is rapidly becoming a viable and exploitable technology where BT is well placed as both a potential user and provider of agent-based services and products.

The highly interactive nature of multi-agent systems points to the need for consensus on agent interfaces in order to support interoperability between different agent systems. The completion and adoption of such a standard is a prerequisite to the commercialisation and successful exploitation of agent technology.

This paper describes FIPA (Foundation for Intelligent Physical Agents) and its organisation. It provides an overview and guide to the FIPA97 specification. It discusses how FIPA relates to other agent standards activities and concludes with FIPA's plans for 1998.

FIPA — Towards a Standard for Software Agents

An introduction to agent technology.- Artificial societies and psychological agents.- Co-ordination in multi-agent systems.- Software agent technologies.- Information agents for the World Wide Web.- Multi-agent matchmaking.- An application of social filtering to movie recommendation.- Agents of change in business process management.- Agents, mobility and multimedia information.- A real-life experiment in creating an agent marketplace.- The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems.- An introduction to soft computing - A tool for building intelligent systems.- Basic concepts of a fuzzy logic data browser with applications.- Towards soft computing.- The rise of machine intelligence.- Intelligent software systems.- Machine intelligibility and the duality principle.

Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications

Software Agents and Soft Computing Towards Enhancing Machine Intelligence

Section 1 - Prospects- From Computing with Numbers to Computing with Words-From Manipulation of Measurements to Manipulation of Perceptions- Bringing AI and Soft Computing Together: A Neurobiological Perspective- Future Directions for Soft Computing- Problems and Prospects in Fuzzy Data Analysis- Soft Agent Computing: Towards Enhancing Agent Technology with Soft Computing- Section 2 - Tools- NEFCLASS-J - A JAVA-Based Soft Computing Tool- Soft Computing for Intelligent Knowledge-Based Systems- Advanced Fuzzy Clustering and Decision Tree Plug-Ins for DataEngineTM- Section 3 - Applications- The Intelligent Assistant: An Overview- The YPA - An Assistant for Classified Directory Enquiries- Intelligent Multimodal User Interface- Communication Management: E-Mail and Telephone Assistants- Time Management in the Intelligent Assistant- Machine Interpretation of Facial Expressions- Modelling Preferred Work Plans

Intelligent Systems and Soft Computing: Prospects, Tools and Applications

“The essence of soft computing is that unlike the traditional, hard computing, soft computing is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: ‘...exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality’. In the final analysis, the role model for soft computing is the human mind.” [1]

An Introduction to Soft Computing - A Tool for Building Intelligent Systems

Apparatus for generating a second one or more sequences of elements after receiving a first one or more sequence of elements. The apparatus comprises identifying means for receiving a first one or more sequence of elements, accessing stored sequences, and identifying which of the stored sequences each of the first one or more received sequences of elements most closely resembles, where the identifying means is operable to output, for each received sequence of elements, an identifying signal indicative of the correspondingly so identified stored sequence. The apparatus further comprises a sequence generator for generating sequences of elements, operable to receive as input a said identifying signal and to generate a sequence of elements in response thereto. The apparatus is adapted to operate such that when a first sequence of elements is input to the identifying means (i), the identifying means identifies a stored sequence that the first sequence of elements most closely represents and outputs to the sequence generator an identifying signal indicative of the same, and the sequence generator (ii) generates a second one or more sequences in response thereto.

Nader Azarmi

Papers

Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications

Software Agents and Soft Computing Towards Enhancing Machine Intelligence

Intelligent Systems and Soft Computing: Prospects, Tools and Applications

An Introduction to Soft Computing - A Tool for Building Intelligent Systems

Apparatus for generating sequences of elements