Catálogo de publicaciones - libros

Many advanced knowledge-based systems with purposes of discovery and/or prediction, such as anticipatory reasoning-reacting systems, legal reasoning systems, geographical information system, and so on, require a forward deduction engine for modal logics as an important component. However, until now, there is no forward deduction engine with the general-purpose for modal logics. This paper presents a general-purpose forward deduction engine for modal logics. We show some essential requirements and functions for a general-purpose forward deduction engine for modal logics, present our implementation of the forward deduction engine, and show possible applications based on our forward deduction engine.

In this paper we study the brokering and matchmaking problem in the tourism domain, that is, how a requester’s requirements and preferences can be matched against a set of offerings collected by a broker. The proposed solution uses the Semantic Web standard of RDF to represent the offerings, and a deductive logical language for expressing the requirements and preferences. We motivate and explain the approach we propose, and report on a prototypical implementation exhibiting the described functionality in a multi-agent environment.

We present a simple, yet general top-down query answering procedure for logic programs managing uncertainty. The main features are: () the certainty values are taken from a certainty lattice; () computable functions may appear in the rule bodies to manipulate certainties; and () we solve the problem by a reduction to an equational systems, for which we device a top-down procedure.

Trust management is a promising approach for the authorization in distributed environment. There are two key issues for a trust management system: how to design high-level policy language and how to solve the compliance-checking problem [3,4]. We adopt this approach to deal with distributed authorization with delegation. In this paper, we propose an , a human-understandable high level language to specify various authorization policies. Language has rich expressive power which can not only specify delegation, and threshold structures addressed in previous approaches, but also represent structured resources and privileges, positive and negative authorizations, separation of duty, incomplete information reasoning and partial authorization and delegation. We define the semantics of through logic programming with answer set semantics and through an authorization scenario we demonstrate the application of language .

This paper describes the architecture and the implementation of the SAMIR (Scenographic Agents Mimic Intelligent Reasoning) system, designed to implement 3D conversational agents. SAMIR uses an XCS classifier system to learn natural facial postures to be shown by the agents, during their dialogues with users. An administration program can be run by in order to fine-tune the agent knowledge base and emotive responses. It results in a flexible approach to dialogue and facial expressions management, fitting the needs of different web applications such as e-shops or digital libraries.

In this paper an intelligent Hierarchical Neural Network system for prediction and modelling of interest rates in Australia is developed. A Hierarchical Neural Network system is developed to model and predict three months (quarterly) interest rate fluctuations. The system is further trained to model and predict interest rates for six months and one-year periods. The proposed system is developed with first four and then five hierarchical Neural Network to model and predict interest rates. Conclusions on the accuracy of prediction using hierarchical neural networks are also reported.

Electricity load forecasting has been the subject of research over the past several years by researchers and practitioners in academia and industry. This is due to its very important role for effective and economic operation of power stations. In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of electricity consumption is developed. A hierarchical fuzzy logic system is developed to model and predict daily electricity load fluctuations. The system is further trained to model and predict electricity consumption for daily peak.

An advisory system has always been a central need for medical practitioners and specialists for the last few decades. Most of current medical retrieval systems are based on passive databases. Actually, the data stored in any information storage system is a rich source of knowledge, which needs appropriate techniques to discover. This paper introduces the design of a well-structured knowledge-base system that holds patient medical records. The proposed system will be equipped with data mining and AI techniques such as statistics, neural network, fuzzy logic, generic algorithm, etc., so that it becomes an active distributed medical advisory system.

A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.

The basic belief-desire-intention (BDI) agent model appears to be inappropriate for building complex system that that must learn and adapt their behaviour dynamically. The contribution of the paper is the introduction of a new “intelligent-Deliberation” process in the hybrid BDI ( h -BD[I]) architecture that enables an improved decision making features in a dynamic, and complex environment. Shared learning vector quantization (SLVQ) based neural network is proposed for the intelligent deliberation of the agent model. Paper discusses the benefits of incorporating knowledge based techniques in the deliberation process of the extended h -BD[I] model.