Catálogo de publicaciones - libros

Along the various editions of EPIA the scientific program comprised invited lectures, tutorials, parallel workshops, and paper presentations. The success of the workshop format, since it was adopted by the conference, motivated the organizers of the previous and current editions to generalize the adoption of this model for scientific presentations, leaving the plenary sessions for invited lectures, tutorials, posters and panels.

As expected, although a signi.cant number of workshops are accepted in each edition of EPIA, they do not cover all areas of AI. Another peculiarity of the workshop format is that the areas that are addressed differ substantially from one edition to another.

The paper studies reductions of propositional theories in equilibrium logic to logic programs under answer set semantics. Specifically we are concerned with the question of how to transform an arbitrary set of propositional formulas into an equivalent logic program and what are the complexity constraints on this process. We want the transformed program to be equivalent in a strong sense so that theory parts can be transformed independent of the wider context in which they might be embedded. It was only recently established [1] that propositional theories are indeed equivalent (in a strong sense) to logic programs. Here this result is extended with the following contributions. (i) We show how to effectively obtain an equivalent program starting from an arbitrary theory. (ii) We show that in general there is no polynomial time transformation if we require the resulting program to share precisely the vocabulary or signature of the initial theory. (iii) Extending previous work we show how polynomial transformations can be achieved if one allows the resulting program to contain new atoms. The program obtained is still in a strong sense equivalent to the original theory, and the answer sets of the theory can be retrieved from it.

Preference criteria are rarely static. Often they are subject to modification and aggregation. The resulting preference criteria may not satisfy the properties of the original ones and must therefore be revised. This paper investigates the problem of revising such preference criteria by means of declarative debugging techniques.

This paper introduces an original 2-valued semantics for Normal Logic Programs (NLP), which conservatively extends the Stable Model semantics (SM) to all normal programs. The distinction consists in the revision of one feature of SM, namely its treatment of odd loops, and of infinitely long support chains, over default negation. This single revised aspect, addressed by means of a approach, affords a number of fruitful consequences, namely regarding existence, relevance and top-down querying, cumulativity, and implementation.

The paper motivates and defines the Revised Stable Models semantics (rSM), justifying and exemplifying it. Properties of rSM are given and contrasted with those of SM. Furthermore, these results apply to SM whenever odd loops and infinitely long chains over negation are absent, thereby establishing significant, not previously known, properties of SM. Conclusions, further work, terminate the paper.

Theoretical research has spent some years facing the problem of how to represent and provide semantics to updates of logic programs. This problem is relevant for addressing highly dynamic domains with logic programming techniques. Two of the most recent results are the definition of the refined stable and the well founded semantics for dynamic logic programs that extend stable model and well founded semantic to the dynamic case. We present here alternative, although equivalent, operational characterizations of these semantics by program transformations into normal logic programs. The transformations provide new insights on the computational complexity of these semantics, a way for better understanding the meaning of the update programs, and also a methodology for the implementation of these semantics. In this sense, the equivalence theorems in this paper constitute soundness an completeness results for the implementations of these semantics.

In this paper we discuss the use of Case Retrieval Nets, a particular memory model for implementing case-base reasoning solutions, for implementing a heuristic lexicalisation module within a natural language generation application. We describe a text generator for fairy tales implemented using a generic architecture, and we present examples of how the Case Retrieval Net solves the Lexicalization task.

In this paper we propose a simple and intuitive method for constructing partially linear models and, in general, partially parametric models, using support vector machines for regression and, in particular, using regularization networks (splines). The results are more satisfactory than those for classical nonparametric approaches. The method is based on a suitable approach to selecting the kernel by relaying on the properties of positive definite functions. No modification is required of the standard SVM algorithms, and the approach is valid for the -insensitive loss. The approach described here can be immediately applied to SVMs for classification and to other methods that use the kernel as the inner product.

Template matching face detection systems are used very often as a previous step in several biometric applications. These biometric applications, like face recognition or video surveillance systems, need the face detection step to be efficient and robust enough to achieve better results. One of many template matching face detection methods uses Hausdorff distance in order to search the part of the image more similar to a face. Although Hausdorff distance involves very accurate results and low error rates, overall robustness can be increased if we adapt it to our concrete application. In this paper we show how to adjust Hausdorff metrics to face detection systems, presenting a scale-normalized Hausdorff distance based face detection system. Experiments show that our approach can perform an accurate face detection even with complex background or varying light conditions.

We are interested in the challenging scientific pursuit of how to characterize human activities in any formal meeting situation by tracking people’s positions with a computer vision system. We present a human activity recognition algorithm that works within the framework of CAMEO (the Camera Assisted Meeting Event Observer), a panoramic vision system designed to operate in real-time and in uncalibrated environments. Human activity is difficult to characterize within the constraints that the CAMEO must operate, including uncalibrated deployment and unmodeled occlusions. This paper describes these challenges and how we address them by identifying invariant features and robust activity models. We present experimental results of our recognizer correctly classifying person data.

Almost forty years ago, Herbert Simon emphasised the role of emotions in problem solving. Nevertheless, until recently, research on intelligent systems has traditionally been focused on the development of theories and techniques mostly inspired on what was considered the “rational” aspects of human behaviour.

But findings from neuroscience (such as by Damásio and LeDoux’s) and psychology suggesting that emotions are a leading part of what is considered intelligent behaviour, has brought the role of emotions into the limelight. Furthermore, the work by R. Picard, and the creation of the area of Affective Computing, has provided the right frame for research and develop new intelligent systems. Emotions can further be considered, not only as essential for problem solving techniques in intelligent systems but also allow for the construction of systems that interact with humans in more natural and human-like manner. Also, the increasing attention given to Agent-oriented programming makes it more relevant the enhancement of agent deliberation on the grounds of both rationality and emotionality.