Catálogo de publicaciones - libros

Autonomous agents or semi-autonomous agents feature largely in their dynamic adaptation of its behaviors for changing environments to achieve some set of goals, especially in ubiquitous environments. It demands mechanism for coherently satisfying agent goals depending on changing availability of resources one the fly. Leveraging context-aware techniques and agent-oriented approaches, the paper proposes a lightweight architecture to build adaptive but predictable goal-driven ubiquitous agent systems based on OSGi (Open Service Gateway initiative), an open standard service-oriented framework. The proposed platform not only supports context acquisition, discovery and reasoning, but also provides a centric goal resolution mechanism using goal-tree to automatically specify service components for the satisfaction of agent goals. A Context Pair Language and the production rule are used as semantic basis to model contextual information as well as the goals.

This paper proposes an Agent-based Collaborative Virtual Environ-ment (ACVE) architecture using grid technologies. In this virtual environment, all e-learning resources and web services are bound via service encapsulation, and a special collaboration service layer undertakes the robust collaborative learning, moreover, the agents and mobile agents are applied to autonomous collaboration, and to management persistency of the virtual world. As for collaboration, the Locking Service, Context Awareness Service, etc are intro-duced to maintain efficiency of collaboration activities; two agents of learning roles are responsible for actual learning interactions, and the Session Agent has the ability of migrating among hosts to finish learning goals as well as maximize resource utilization. The GT4.0, JADE and a WS proxy are used to implement all functions. The result suggests it will be a more scalable and robust collaborative learning architecture.

Music recommendation systems used at the present time apply certain queries using appropriate music information or user profiles in order to obtain the desired results. However, these systems are unable to satisfy user desires because these systems only reply to the results of user queries or consider static information, such as a user’s sex and age. In order to solve these problems, this paper attempts to define context information to select music and design a music recommendation system that is suited to a user’s interests and preferences using a filtering method. The recommendation system used in this study uses an Open Service Gateway Initiative (OSGi) framework to recognize context information. Not only does this framework promote a higher user satisfaction rate for music recommendations, service quality is also improved by applying service mobility and distributed processing.

Evolution of World Wide Web has made it a focal point of research to apply structured and well-defined meanings to achieve Semantic Web and ultimately the Semantic Grid. The realization of this goal gives rise to the integration of technologies that can negotiate and cooperate in which agents have proven to be effective. OWL (Web Ontology Language) is currently the W3C standard for providing the explicit semantics to the web services whereas the FIPA Semantic Language is the core of agent platforms due to its high expressive power. The key objective of this paper is the development of architecture and semantic translations in such a way that the agents can communicate with the web services in an efficient manner.

This paper presents an agent-based adaptive task-scheduling model for pure P2P computational grids, in which the task-scheduling mechanism is recursive, dependable and purely decentralized. The main idea is that the application provides the task decomposing method and decomposition is triggered by the platform once parallel running is possible. Mobile agents are used to carry tasks and results moving from one node to another. Each node has a manager agent administrating other agents to distribute and schedule tasks. From the preliminary testing results, it can be seen that the model can provide a low-cost large-scale computing platform on the pure P2P architecture. It avoids the disadvantages of hybrid-P2P architectures and is easy to adapt to the applications that can be decomposed into independent coarse-grained subtasks.

This study proposes a cluster-based data transmission solution for ad hoc networks. In the proposed concept, when a cluster is formed, cluster head receives the trust values of neighboring nodes and evaluates the trust of cluster member nodes in an accurate and efficient way using trust values it received and its own leader value. This mechanism enables cluster head to authenticate new nodes randomly joining the cluster. In addition, a limited broadcasting mechanism was used by reducing the amount of control packets in an effort to enhance the efficiency in data transmission. The number of cluster heads, the communication path length and the amount of control packets were measured to verify the efficiency of the proposed concept.

Embodied conversational agents (ECA’s) are cartoon-like characters which interact with users through conversation and gestures on a computer screen. ECA makes human computer interactions more friendly because we can use most human-like communication skills such as natural conversation. ECA’s are useful as Web guides by incorporating them into Web browsers. They guide us around Web pages chatting with us. To build such an agent, we need to describe a scenario to explain Web pages. Conventionally such scenarios are written manually by developers or programmers using a dialogue description language such as AIML (Artificial Intelligence Markup Language), so it is difficult to update them when Web pages are updated. In this paper, we propose a scheme to automatically generate utterances of Web guide agents depending on Web pages. To this end, we need to make agents understand the contents of Web pages and to make them talk according to the contents, so we utilize RDF (Resource Description Framework) to present the semantic contents of Web pages. To make agents talk according to the contents, we utilize a RDF query language SPARQL (Simple Protocol And RDF Query Language) and extend the AIML language to incorporate SPARQL query in it. As a prototype, we developed a Web guide system employing an ECA.

There are a lot of services in ubiquitous computing environments. So, ubiquitous service need a service matchmaker which presents more easily and with accuracy. The coupling of webservices and semantic web technology provides the ability to automatically discover, compose and execute webservices. However, the composition of services is generally static because these services are usually described using BPEL4WS or WSFL, restricting dynamic operation because the composite service only has a sequence execution plan. This dynamic composition cannot generate a parallel execution plan for many Internet business applications. In this paper, we design an ontology based framework for dynamic webservice composition. Also, we present a semantic webservice framework using dynamic composition model. This dynamic composition model can generate a parallel execution plan. These plans are calculated using QoS model, hence the best execution plan is selected.

In traditional business model, the buying decision process is poorly coordinated among the human decision-makers. Therefore, a long-lived, adaptive, and autonomous application called software agents, that can perform tasks such as personalization, brokering, and negotiation in e-commerce is much needed. These applications reside at the buyers’ side or at the sellers’ servers. The purpose of this paper is to research into possible deployment of software agents in a framework for e-commerce buying decision process. This paper overviews the traditional business model, the Consumer Buying Behavior (CBB) model, and also covers the requirements needed for minimizing human interactions in buying decision processes. The research proposes a software agent’s framework in which, two main approaches, namely Automated Collaborative Filtering (ACF) and Better Business Bureau (BBB), are merged to produce better agents in assisting buying decision process. The framework will enable the agents to get the best price for a good product from a reputable merchant.

The rapid emergence of small connected devices with wireless links needs highly dynamic adaptable distributed system architectures. But, in most current mobile agent systems, each mobile agent is able to exploit only uniform functionalities supported in every runtime environment. This feature has the agent difficult to use environment-specific resources. This paper presents a transparently dynamic adaptation framework using aspect oriented programming technique to adjust not only a variety of static resources, but also dynamic ones whose amount is continually changed at runtime even in the same computational environment. To make agent programmers easy to implement applications with no knowledge of dynamic adaptation, software developers in the proposed framework are classified into three groups, mobile agent application programmer, policy decision maker and component implementer. In here, policy decision makers can apply various adaptation policies to dynamically changing environments in order to accommodate mobile agents to the change of their resources.