Catálogo de publicaciones - libros

Agents teamwork is a sub-area of multi-agent systems that is mainly composed of artificial intelligence and distributed computing techniques. Due to its inherent complexity, many theoretical and applied techniques have been applied to the investigation of agent team architecture with respect to coordination, cooperation, and learning skills. In this paper, we discuss agent team architecture and analyse how to adapt the simulation system for investigating agent team architecture, learning abilities, and other specific behaviors.

Research in Multi-Agent Systems has revealed that Agents must enter into a relationship voluntarily in order to collaborate, otherwise that collaborative efforts may fail [1,2]. When examining this problem, trust becomes the focus in promoting the ability to collaborate, however trust itself is defined from several perspectives. Trust between agents within Multi-Agent System may be analogous to the trust that is required between humans. A Trust, Negotiation, Communication model currently being developed, is based around trust and may be used as a basis for future research and the ongoing development of Multi-Agent System (MAS).

This paper is focused on discussing how the architecture of an agent could be designed to provide it the ability to foster trust between agents and therefore to dynamically organise within a team environment or across distributed systems to enhance individual abilities. The Trust, Negotiation, Communication (TNC) model is a proposed building block that provides an agent with the mechanisms to develop a formal trust network both through cooperation or confederated or collaborative associations. The model is conceptual, therefore discussion is limited to the basic framework.

This paper introduces the invited session of “Intelligent Agents and their Applications” being presented at the 10 International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. This session concentrates on discussing Intelligent Agents and uses some applications to demonstrate the theories reported. An update on last years session [1] is included to report on several key advances in technology that further enable the ongoing development of multi-agent systems. A brief summary of the impediments presented to researchers is also provided to highlight why innovation must be used to sustain the evolution of Artificial Intelligence. This year a variety of challenges and concepts are presented, together with a collection of thoughts about the direction and vision of Intelligent Agents ands their Applciations.

The ubiquitous computing technology utilizing multi-agent system provides services suitable to the given situation without the restraint of time and space. Agent-oriented software engineering (AOSE) emerges to build a multi agent system in a large scale and capable of accommodating communications between diverse agents. Building on a meta-model of the community computing for the abstraction of agent system design in a previous work, our work focuses on a structure of community computing middleware leading to the extension of the system. The middleware proposed enables the framework of community computing, that is, a cooperative multi-agent approach, to be constituted in a dynamic fashion.

Grid computing is a computing framework to meet the growing computational demands. This paper introduces a novel approach based on Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. The proposed approach is to dynamically generate an optimal schedule so as to complete the tasks within a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of the proposed PSO algorithm with Genetic Algorithm (GA) and Simulated Annealing (SA) approaches.

There is consensus amongst aviation researchers and practitioner that some 70% of all aircraft accidents have human error as a root cause [1]. Thatcher, Fyfe and Jain [2] have suggested an intelligent landing support system, comprising of three agents, that will support the flight crew in the most critical phase of a flight, the approach and landing. The third agent is envisaged to act as a pattern matching agent or an ‘extra pilot’ in the cockpit to aid decision making. This paper will review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [3]. But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts [4]. We show visualisation results on some real and artificial data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data.

Information retrieval (IR) is challenging for a non-expert who operates in a technical area such as medical terminology. Since IR is essentially a decision making process, experience with the design of intelligent decision support systems is relevant. Intelligent agents can assist the user during decision making, and, by extension, in IR to locate the desired information. This paper presents an extension to an IR system for a medical application in which the user lacks the descriptive vocabulary needed to retrieve the resources. Agents continuously seek information on behalf of the user and are autonomous, proactive, communicative, mobile, goal-driven and persistent in order to retrieve information on behalf of the user.

This paper provides a description of pulse processing and signal identification techniques in Radar Warning Receiver and Electronic Support Measures systems with the objective of describing their similarity to certain Artificial Intelligence techniques. It also presents aspects for which future developments in artificial intelligence based techniques could support the objectives of such systems, both during operation and during the more detailed analysis of data after operations and in counteracting future trends in radar developments. These include parameter optimization, learning and predicting outcomes related to unseen data and so on.

Artificial Intelligence techniques are increasingly extending and enriching decision support through such means as coordinating data delivery, analyzing data trends, providing forecasts, developing data consistency, quantifying uncertainty, anticipating the user’s data needs, providing information to the user in the most appropriate forms, and suggesting courses of action. This session of the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems focuses on the use of Artificial Intelligence to enhance decision making.

Recent advances in the area of mobile robotics caused growing attention of the armed forces, where the necessity for unmanned vehicles being able to carry out the “dull and dirty” operations, thus avoid endangering the life of the military personnel. UAV offers a great advantage in supplying reconnaissance data to the military personnel on the ground, thus lessening the life risk of the troops. In this paper we analyze various techniques for path planning and obstacle avoidance and cooperation issues for multiple mobile robots. We also present a generic dynamics and control model for steering a UAV along a collision free path from a start to a goal position.