Catálogo de publicaciones - libros

The integration between emotion and cognition can provide an important support for adaptation and decision-making under resource-bounded conditions, typical of real-world domains. The ability to adjust cognitive activity and to take advantage of emotion-modulated memories are two main aspects resulting from that integration. In this paper we address those issues under the framework of the , describing the formation of emotional memories and the regulation of their use through attention focusing. Experimental results from simulated rescue scenarios show how the proposed approach enables effective decision making and fast adaptation rates in completely unknown environments.

Moods and emotions influence human reasoning, most of the time in a positive way. One aspect of reasoning is the revision of beliefs, i.e., how to change a set of beliefs in order to incorporate new information that conflicts with the existing beliefs. We incorporate two influences of affective states on belief maintenance identified by psychologists, in a AI belief revision operation. On one hand, we present an alternative operation to conventional Belief Revision, Affective Revision, that determines the preference between new and old information based on the mood of the agent revising its beliefs. On the other, we show how beliefs can be automatically ordered, in terms of resistance to change, based on (among other aspects) the influence of emotion anticipations on the strength of beliefs.

Interactive virtual environments (IVEs) are now seen as an engaging new way by which children learn experimental sciences and other disciplines. These environments are populated by synthetic characters that guide and stimulate the children activities. In order to build such environments, one needs to address the problem of how achieve believable and empathic characters that act autonomously. Inspired by the work of traditional character animators, this paper proposes an architectural model to build autonomous characters where the agent’s reasoning and behaviour is influenced by its emotional state and personality. We performed a small case evaluation in order to determine if the characters evoked empathic reactions in the users with positive results.

In this part we present accepted communications to ALEA’05, which took place at University of Covilhã, Portugal, on 5-8 December 2005. ALEA’05 was the second workshop on Artificial Life and Evolutionary Algorithms, organised as part of EPIA’05 (Portuguese Conference on Artificial Intelligence). ALEA is an event targeted at Artificial Life (ALife) community, Evolutionary Algorithms (EA) community and researchers working in the crossing of these two areas.

To a certain extent, ALife and EA aim at similar goals of classical Artificial Intelligence (AI): to build computer based intelligent solutions. The path, however, is diverse since ALife and EA are more concerned with the study of simple, bottom-up, biologically inspired modular solutions. Therefore, research on computer based bio-inspired solutions, possibly with emergent properties, and biology as computation, may be the global characterisation of this workshop.

This paper proposes and analyzes different evolutionary computation techniques for conjointly determining a model and its associated parameters. The context of 3D reconstruction of objects by a functional representation illustrates the ability of the proposed approaches to perform this task using real data, a set of 3D points on or near the surface of the real object. The final recovered model can then be used efficiently in further modelling, animation or analysis applications. The first approach is based on multiple genetic algorithms that find the correct model and parameters by successive approximations. The second approach is based on a standard strongly-typed implementation of genetic programming. This study shows radical differences between the results produced by each technique on a simple problem, and points toward future improvements to join the best features of both approaches.

In the context of multi-agent simulation of biological complex systems, we present a reaction-agent model for biological chemical kinetics that enables interaction with the simulation during the execution. In a chemical reactor with no spatial dimension -e.g. a cell-, a reaction-agent represents an autonomous chemical reaction between several reactants : it reads the concentration of reactants, adapts its reaction speed, and modifies consequently the concentration of reaction products. This approach, where the simulation engine makes agents intervene in a chaotic and asynchronous way, is an alternative to the classical model -which is not relevant when the limits conditions change- based on differential systems. We establish formal proofs of convergence for our reaction-agent methods, generally quadratic. We illustrate our model with an example about the extrinsic pathway of blood coagulation.

A number of studies have been performed with the objective of applying various artificial intelligence techniques to the prediction and classification of cancer specific biomarkers for use in clinical diagnosis. Most biological data, such as that obtained from SELDI-TOF (Surface Enhanced Laser Desorption and Ionization-Time Of Flight) MS (Mass Spectrometry) is high dimensional, and therefore requires dimension reduction in order to limit the computational complexity and cost. The DT (Decision Tree) is an algorithm which allows for the fast classification and effective dimension reduction of high dimensional data. However, it does not guarantee the reliability of the features selected by the process of dimension reduction. Another approach is the MLP (Multi-Layer Perceptron) which is often more accurate at classifying data, but is not suitable for the processing of high dimensional data. In this paper, we propose on a novel approach, which is able to accurately classify prostate cancer SELDI data into normal and abnormal classes and to identify the potential biomarkers. In this approach, we first select those features that have excellent discrimination power by using the DT. These selected features constitute the potential biomarkers. Next, we classify the selected features into normal and abnormal categories by using the MLP; at this stage we repeatedly perform cross validation to evaluate the propriety of the selected features. In this way, the proposed algorithm can take advantage of both the DT and MLP, by hybridizing these two algorithms. The experimental results demonstrate that the proposed algorithm is able to identify multiple potential biomarkers that enhance the confidence of diagnosis, also showing better specificity, sensitivity and learning error rates than other algorithms. The proposed algorithm represents a promising approach to the identification of proteomic patterns in serum that can distinguish cancer from normal or benign and is applicable to clinical diagnosis and prognosis.

How to find a sufficient number of uniformly distributed and representative Pareto optimal solutions is very important for Multiobjective Optimization (MO) problems. An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which competition and clonal selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal Front. An improved measure for uniformity is carried out to the approximation of the Pareto-optimal set. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive.

This paper provides a new proposal that aims to solve multi-objective optimization problems (MOP) using quantum evolutionary paradigm. Three main features characterize the proposed framework. In one hand, it exploits the states superposition quantum concept to derive a probabilistic representation encoding the vector of the decision variables for a given MOP. The advantage of this representation is its ability to encode the entire population of potential solutions within a single chromosome instead of considering only a gene pool of individuals as proposed in classical evolutionary algorithms. In the other hand, specific quantum operators are defined in order to reward good solutions while maintaining diversity. Finally, an evolutionary dynamics is applied on these quantum based elements to allow stochastic guided exploration of the search space. Experimental results show not only the viability of the method but also its ability to achieve good approximation of the Pareto Front when applied on the multi-objective knapsack problem.

The emergence of the Semantic Web has marked another stage in the evolution of the ontology field. According to Berners-Lee, the Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. This cooperation can be achieved by using shared knowledge-components. Therefore ontologies have become a key instrument in developing the Semantic Web. They interweave human understanding of symbols with their machine processability.

This workshop addressed the problems of building and applying ontologies in the semantic web as well as the theoretical and practical challenges arising from different applications. We invited and received contributions that enhance the state-of-the-art of creating, managing and using ontologies. The workshop received high quality submissions, which were peer-reviewed by two or three reviewers.