Catálogo de publicaciones - libros

Preferences play a major role in many AI applications. We give a brief overview of methods for adding qualitative preferences to answer set programming, a promising declarative programming paradigm. We show how these methods can be used in a variety of different applications such as configuration, abduction, diagnosis, inconsistency handling and game theory.

Map reconstruction and robot location are two essential problems in the field of robotics and artificial intelligence. A robot could need a model of the environment that can be incomplete and therefore the robot must work considering the uncertainty. Bayesian Units consider the uncertainty and allow the fusion of information from different sensors. In this paper a map reconstruction system in 3D based on Bayesian Units is presented. The reconstruction is carried out integrating the data obtained by a laser and by an omnivision system. In addition, to improve the quality of the reconstruction, the fusion of several Bayesian Units is defined using a competitive fusion operator. Finally, the obtained results as well as the validity of the system are shown.

In this paper we present an evolutionary strategy for the multidimensional 0–1 knapsack problem. Our algorithm incorporates a flipping local search process in order to locally improve the obtained individuals and also, a heuristic operator which computes problem-specific knowledge, based on the surrogate multipliers approach introduced in [12]. Experimental results show that our evolutionary algorithm is capable of obtaining high quality solutions for large size problems and that the local search procedure significatively improves the final obtained result.

The Multivariable Fuzzy Temporal Profile model enables human experts to project their knowledge of a signal pattern, described over a set of parameters, into a computable description. Here it is endowed with a hierarchical capability through the definition of algorithms that recognize a set of findings over a signal, and aggregate them into a set of abstraction levels. We also present a heuristics that takes advantage of the continuity properties of real signals to allow the matching process to meet real time requirements, even over high frequency signals.

This paper defines a meta-reasoning model for real-time agents. The purpose of this model is to increase the adaptability in this kind of agents. This model allows to adapt the agent’s behavior as well as the agent’s own reasoning process. The application of this model to an specific real-time agent architecture is also presented.

We propose an efficient method that obtains train timetables. It solves conflicts among trains by assigning priorities for each track section of their journey. The heuristic used to determine the priority for each train, takes into account the objective function of the problem. With this method, we try to explore different regions of the same search space as soon as possible so that the final user obtains a feasible solution in a reasonable computation time.

Model-based diagnosis enables isolation of faults of a system. The diagnosis process uses a set of sensors (observations) and a model of the system in order to explain a wrong behaviour. In this work, a new approach is proposed with the aim of improving the computational complexity for isolating faults in a system. The key idea is the addition of a set of new sensors which allows the improvement of the diagnosability of the system. The methodology is based on constraint programming and a greedy method for improving the computational complexity of the CSP resolution. Our approach maintains the requirements of the user (detectability, diagnosability,...).

Perception triggered response behavior together with the selection of the appropriate environmental cues can properly guide the robot towards its goal. But often landmarks are difficult to be recognised by the robot; these difficulties are increased due to the morphology of the robot and its movements. Dynamic orientation of the camera’s parameters, fixing the attention of the visual system on relevant environmental features, can greatly improve vision based landmark identification. This paper presents an approach to sonar and compass based dynamic selection of pan, tilt and zoom values.

This paper presents experimental results of our model-driven approach to agent based simulation. According to this, the development process for agent based simulation should focus on modeling rather than implementation (i.e., programming on some concrete agent simulation platform). This requires the use of transformation tools from simulation models to implementation code. Describing social phenomena with a visual, high-level modeling language and implementing the simulation in this way should facilitate the use of toolkits by experts in social sciences without a deep knowledge of programming concerns. This simulation approach is supported here by the INGENIAS Development Kit (IDK), which provides a model editor for multi-agent systems, and code generation support. To validate the transformation mechanism, we have modeled a concrete social system with the INGENIAS agent-oriented modeling language, and generated two independent implementations for two different platforms (Repast and Mason simulation toolkits). This experimentation shows the feasibility of the model driven implementation approach and has enabled the study of facilities provided by simulation toolkits that can have impact on the transformation process, in particular, the scheduling techniques. Also, comparing the simulation results of the case study with the original work and between implementations has led us to discover biases introduced by simulation mechanisms that can be found in most simulation platforms.

Nowadays, we have different agent oriented methodologies that enable developers to produce agent oriented designs. One of the recurrent problems of these methodologies is how to describe the behaviour of agents within a system. A developer needs primitives to express autonomy, proactivity, and social concerns of his agents, but there are problems in understanding what does these elements mean, beyond any natural language explanation. There is a clear need of semantic models understandable by average engineers. These models could help in foreseeing the impact of autonomy with respect system goals, or determining if, in an agent specification, a task will ever be scheduled for execution. This paper presents a proposal of semantic model for the visual modelling language used in INGENIAS, a project started in 2002 and considered the inheritor of MESSAGE/UML.