Catálogo de publicaciones - libros

Recently, there is a massive use of the agent technology to develop the intelligent software and smart control module. In this paper, we propose a support tool for Multi-Agent Development, which enables the overall development time to be reduced. The proposed tool provides the general architecture constructed by components, interfaces, and functions of an agent an It also provides the extended Message Sequence Diagram for the communication of Multi-Agent. With the aid of proposed tool, developers who are inexperience in agent development can design and implement Multi-Agent rapidly.

This paper highlights the Hybrid agent construction model being developed that allows the description and development of autonomous agents in SAGE (Scalable, fault Tolerant Agent Grooming Environment) – a second generation FIPA-Compliant Multi-Agent system. We aim to provide the programmer with a generic and well defined agent architecture enabling the development of sophisticated agents on SAGE, possessing the desired properties of autonomous agents – reactivity, pro-activity, social ability and knowledge based reasoning.

This research investigates transitive dependence relations, an extension of direct dependence relations, in multi-agent systems. In this paper, action dependence relations are employed to deduct transitive dependence relations from direct dependence relations. Transitive dependence is useful in representation, analysis, and social relations reasoning between agents, groups, organizations, etc. Furthermore, in this paper, dependence relations are differentiated by both dependence property and dependence degree, which is useful in quantitative social reasoning.

Conventional adaptive systems have common well-known constraints when attempting to normalize environment. An adaptive system must contain a certain number of rules allowing such a system to adapt to specific situations. If there is an absence of a rule in a new situation, the system cannot take appropriate action. Building and managing such complex static adaptive systems places an enormous burden on system developers. In this paper, we propose a multi-agent based intelligent adaptive system with a self-growing engine. In this system, the evaluates input context with specific factors and analyzes the results. The selects the most appropriate action among alternatives available for a specific context and intelligently evolves and adapts by means of a self-growing engine (SGE). The SGE can evaluate actions and generate new rules by applying it to a practical situation using remote video conferencing with mobile devices such as PDAs, and PCs.

Resources or devices on the network might be unavailable due to serious network partitioning. To provide a robust network connectivity, this paper presents an autonomous and dependable recovery scheme using teamwork in UPnP network settings. For our scheme, we introduce a team of recoverable control points and corresponding recovery device, and, in case of network failures among devices, the recoverable control points autonomously take care of the devices to achieve their mutual goal as a team member. In the experiments, we tested our recovery scheme in terms of the recovery effectiveness. It turned out that the recoverable control point agent in our scheme successfully handled the events from the devices, regardless of one of recoverable control points being killed. We argue that our recovery scheme is fairly consistent, and the control point agents can recover from failures as quickly as possible.

Coalition formation in multi-agent systems (MAS) is becoming increasingly important as it increases the ability of agents to execute tasks and maximize their payoffs. This paper proposes a novel dependence theory namely transitive dependence theory for dynamic coalition formation in multi-agent system. Based on the proposed transitive dependence theory, a reasoning mechanism for searching coalition partners has been worked out which includes dependence tree generation, dependence tree reduction, plan optimization and action optimization.

There is increasing demand for the self-diagnosis and self-healing of problems or errors arising in systems operating in the ubiquitous computing environment. In this paper, we propose a self-healing system that monitors, diagnoses and heals its own problems. The proposed system consists of multi agents that analyze the log context in order to perform self-diagnosis and self-healing. To minimize the resources used by the in an existing system, we place a single process in memory. By this, we mean that a single monitors the context of the logs that are generated by the different components of the system. For rapid and efficient self-healing, we use a process. The effectiveness of the proposed system is confirmed through experiments conducted with a prototype system.

Agents make up an important part of game worlds, ranging from the characters and monsters that live in the world to the armies that the player controls. Despite their importance, agents in current games rarely display an awareness of their environment or react appropriately, which severely detracts from the believability of the game. Some games have included agents with a basic awareness of other agents, but they are still unaware of important game events or environmental conditions. This paper presents an agent design we have developed, which combines cellular automata for environmental modeling with influence maps for agent decision-making. The agents were implemented into a 3D game environment we have developed, the EmerGEnT system, and tuned through three experiments. The result is simple, flexible game agents that are able to respond to natural phenomena (e.g. rain or fire), while pursuing a goal.

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called . Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such ‘pattern languages’ would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of will provide an invaluable resource for both practicing and future generations of modelers.

In this paper, a new crossover operator based on Latin square design is presented at first. This crossover operator can generate a set of uniformly scattered offspring around their parents, and it is of the ability of local search and thus can explore the search space efficiently. Then the level set of the objective function is evolved successively by crossover and mutation operators such that it gradually approaches to global optimal solution set. Based on these, a new evolutionary algorithm for nondifferentiable unconstrained global optimization is proposed and its global convergence is proved. At last, the numerical simulations are made for some standard test functions. The performance of the proposed algorithm is compared with that of two widely-cited algorithms. The results indicate the proposed algorithm is effective and has better performance than the compared algorithms for these test functions.