Catálogo de publicaciones - libros

In this paper we focus on the problem of belief aggregation, i.e. the task of forming a group consensus probability distribution by combining the beliefs of the individual members of the group. We propose the use of Bayesian Networks to model the interactions between the individuals of the group and introduce average and majority canonical models and their application to information aggregation. Due to efficiency restrictions imposed by the Group Recommending problem, where our research is framed, we have had to develop specific inference algorithms to compute group recommendations.

In the score plus search based Bayesian networks structure learning approach, the most used method is hill climbing (HC), because its implementation is good trade-off between CPU requirements, accuracy of the obtained model, and ease of implementation. Because of these features and to the fact that HC with the classical operators guarantees to obtain a minimal I-map, this approach is really appropriate to deal with high dimensional domains. In this paper we revisited a previously developed HC algorithm (termed constrained HC, or CHC in short) that takes advantage of some scoring metrics properties in order to restrict during the search the parent set of each node. The main drawback of CHC is that there is no warranty of obtaining a minimal I-map, and so the algorithm includes a second stage in which an unconstrained HC is launched by taking as initial solution the one returned by the constrained search stage. In this paper we modify CHC in order to guarantee that its output is a minimal I-map and so the second stage is not needed. In this way we save a considerable amount of CPU time, making the algorithm best suited for high dimensional datasets. A proof is provided about the minimal I-map condition of the returned network, and also computational experiments are reported to show the gain with respect to CPU requirements.

Message-passing inference algorithms for Bayes nets can be broadly divided into two classes: i) clustering algorithms, like Lazy Propagation, Jensen’s or Shafer-Shenoy’s schemes, that work on secondary undirected trees; and ii) conditioning methods, like Pearl’s, that use directly Bayes nets. It is commonly thought that algorithms of the former class always outperform those of the latter because Pearl’s-like methods act as particular cases of clustering algorithms. In this paper, a new variant of Pearl’s method based on a secondary directed graph is introduced, and it is shown that the computations performed by Shafer-Shenoy or Lazy propagation can be precisely reproduced by this new variant, thus proving that directed algorithms can be as efficient as undirected ones.

This paper deals with decision-making under uncertainty when the worth of acts is evaluated by means of Sugeno integral on a finite scale. One limitation of this approach is the coarse ranking of acts it produces. In order to refine this ordering, a mapping from the common qualitative utility and uncertainty scale to the reals is proposed, whereby Sugeno integral is changed into a Choquet integral. This work relies on a previous similar attempt at refining possibilistic preference functionals of the max-min into a so-called big-stepped expected utility, encoding a very refined qualitative double lexicographic ordering of acts.

The representation of both scales of cost and scales of benefit is very natural in a decision-making problem: scales of evaluation of decisions are often bipolar. The aim of this paper is to provide algebraic structures for the representation of bipolar rules, in the spirit of the algebraic approaches of constraint satisfaction. The structures presented here are general enough to encompass a large variety of rules from the bipolar literature, as well as having appropriate algebraic properties to allow the use of CSP algorithms such as forward-checking and algorithms based on variable elimination.

is a convenient tool for representing and reasoning with “basic” preferences. However, this logic presents some limitations when dealing with complex preferences that, for instance, involve negated preferences. This paper proposes a new logic that correctly addresses QCL’s limitations. It is particularly appropriate for handling prioritized preferences, which is very useful for aggregating preferences of users having different priority levels. Moreover, we show that any set of preferences, can equivalently be transformed into a set of normal form preferences from which efficient inferences can be applied.

Dung’s abstract theory of argumentation has become established as a general framework for non-monotonic reasoning, and, more generally, reasoning in the presence of conflict. In this paper we extend Dung’s theory so that an argumentation framework distinguishes between: 1) attack relations modelling different notions of conflict; 2) arguments that themselves claim preferences, and so determine defeats, between other conflicting arguments. We then define the acceptability of arguments under Dung’s extensional semantics. We claim that our work provides a general unifying framework for logic based systems that facilitate defeasible reasoning about preferences. This is illustrated by formalising argument based logic programming with defeasible priorities in our framework.

Conditional preference networks (CP-nets) are a simple approach to the compact representation of preferences. In spite of their merit the application of the ceteris paribus principle underlying them is too global and systematic and sometimes leads to questionable incomparabilities. Moreover there is a natural need for expressing default preferences that generally hold, together with more specific ones that reverse them. This suggests the introduction of priorities for handling preferences in a more local way. After providing the necessary background on CP-nets and identifying the representation issues, the paper presents a logical encoding of preferences under the form of a partially ordered base of logical formulas using a discrimin ordering of the preferences. It is shown that it provides a better approximation of CP-nets than other approaches. This approximation is faithful w.r.t. the strict preferences part of the CP-net and enables a better control of the incomparabilites. Its computational cost remains polynomial w.r.t. the size of the CP-net. The case of cyclic CP-nets is also discussed.

Problems of conceptual uncertainty have been dealt with in theories of formal logic. Such theories try to accommodate vagueness in two main ways. One is fuzzy logic that introduces degrees of truth. The other way of accommodating formal logic to vagueness is super valuations and its descendants. This paper studies a more inclusive class of reasoning support than formal logic. In the present approach, conceptual uncertainty, including vagueness is represented as higher order uncertainty. A taxonomy of epistemic and conceptual uncertainty is provided. Finally, implications of conceptual uncertainty for reasoning support systems are analyzed.

In this paper, we deal with information exchange policies that may exist in multi-agent systems in order to regulate exchanges of information between agents. More precisely, we discuss two properties of information exchange policies, that is the consistency and the completeness. After having defined what consistency and completeness mean for such policies, we propose two methods to deal with incomplete policies.