Catálogo de publicaciones - libros

We consider advanced policy description specifications in the context of Answer Set Programming (ASP). Motivated by our application scenario, we further extend an existing policy description language, so that it allows for expressing preferences among sets of objects. This is done by extending the concept of ordered disjunctions to cardinality constraints. We demonstrate that this extension is obtained by combining existing ASP techniques and show how it allows for handling advanced policy description specifications.

We present a Hidden Markov Model that uses qualitative order of magnitude probabilities for its states and transitions. We use the resulting model to construct a formalization of qualitative spatiotemporal events as random processes and utilize it to build high-level natural language description of change. We use the resulting model to show an example of foreseen usage of well-known prediction and recognition techniques used in Hidden Markov Models to perform useful queries with the representation.

The planning and scheduling activities are viewed profoundly important to generate successful plans and to maximize the utilization of scarce resources. Moreover, real life planning problems often involve several objectives that should be simultaneously optimized and real world environment is usually characterized by uncertain and incontrollable information. Thus, finding feasible and efficient plans is a considerable challenge. In this respect, theMulti-Objective Resource-Constrained Project- Scheduling problem (RCPSP) tries to schedule activities and allocate resources in order to find an efficient course of actions to help the project manager and to optimize several optimization criteria. In this research, we are developing a new method based on Ant System meta-heuristic and multi-objective concepts to raise the issue of the environment uncertainty and to schedule activities. We implemented and ran it on various sizes of the problem. Experimental results show that the CPU time is relatively short. We have also developed a lower bound for each objective in order to measure the degree of correctness of the obtained set of potentially efficient solutions. We have noticed that our set of potentially efficient solutions is comparable with these lower bounds. Thus, the average gap of the generated solutions is not far from the lower bounds.

When several agents operate in a common environment, their plans may interfere so that the predicted outcome of each plan may be altered, even if it is composed of deterministic actions, only. Most of the multi-agent planning frameworks either view the actions of the other agents as exogeneous events or consider goal sharing cooperative agents. In this paper, we depart from such frameworks and extend the well-known single agent framework for classical planning to a multi-agent one. Focusing on the two agents case, we show how valuable plans can be characterized using game-theoretic notions, especially Nash equilibrium.

Boolean games are a logical setting for representing static games in a succinct way, taking advantage of the expressive power and conciseness of propositional logic. A Boolean game consists of a set of players, each of them controls a set of propositional variables and has a specific goal expressed by a propositional formula. There is a lot of graphical structures hidden in a Boolean game: the satisfaction of each player’s goal depends on players whose actions have an influence on these goals. Even if these dependencies are not specific to Boolean games, in this particular setting they give a way of finding simple characterizations of Nash equilibria and computing them.

Choquet or Sugeno fuzzy integrals are commonly used for aggregating the results of different classifiers. However, both these integrals belong to a more general class of fuzzy t-conorm integrals. In this paper, we describe a framework of a fuzzy t-conorm integral and its use for combining classifiers. We show the advantages of this approach to classifier combining in several benchmark tests.

Weighted fuzzy logic programs increase the expressivity of fuzzy logic programs by allowing the association of a significance weight with each atom in the body of a fuzzy rule. In this paper, we propose a prototype system for the practical integration of weighted fuzzy logic programs with relational database systems in order to provide efficient query answering services. In the system, a dynamic weighted fuzzy logic program is a set of rules together with a set of database queries, fuzzification transformations and fact derivation rules, which allow the provided set of rules to be augmented with a set of fuzzy facts retrieved from the underlying databases. The weights of the rules may be estimated by a neural network-based machine learning process using some specially designated for this purpose training database data.

The problem of averaging outcomes under several scenarios to form overall objective functions is of considerable importance in decision support under uncertainty. The fuzzy operator defined as the so-called Weighted OWA (WOWA) aggregation offers a well-suited approach to this problem. The WOWA aggregation, similar to the classical ordered weighted averaging (OWA), uses the preferential weights assigned to the ordered values (i.e. to the worst value, the second worst and so on) rather than to the specific criteria. This allows one to model various preferences with respect to the risk. Simultaneously, importance weighting of scenarios can be introduced. In this paper we analyze solution procedures for optimization problems with the WOWA objective function. A linear programming formulation is introduced for optimization of the WOWA objective with monotonic preferential weights. Its computational efficiency is analyzed.

In this work, we address the transposition of a fragment of the modeling of the Sociology of Organized Action to the fuzzy setting. We present two different ways of developing fuzzy models in this context, that depend on the kind of available data furnished by the user: one based on the extension principle and another using fuzzy rule-based inference with similarity relations. We illustrate our approach with an example from the sociology literature.

The aim of the paper is to extend the Savage like axiomatization of possibilistic preference functionals in qualitative decision theory to conditional acts, so as to make a step towards the dynamic decision setting. To this end, the de Finetti style approach to conditional possibility recently advocated by Coletti and Vantaggi is exploited, extending to conditional acts the basic axioms pertaining to conditional events.