Catálogo de publicaciones - libros

The main goal of requirements specification is the transformation of a “rough draft” of stakeholder needs and expectations into a semi-formal specification, represented by several diagrams, commonly UML diagrams. These diagrams must be consistent with each other, but consistency among different UML diagrams is not defined by the UML specification, and the research about inter-model consistency is still immature. We propose, in this paper, a rule-based system to detect consistency problems among UML diagrams. In order to complete this task, we have defined a set of rules in OCL, and then we use a novel approach for implementing the system by means of Xquery and Xpath languages. The use of these languages helps the rule-based system to interact with traditional CASE tools.

When information comes from different sources inconsistent beliefs may appear. To handle inconsistency, several model-based belief merging operators have been proposed. Starting from different belief bases which might conflict, these operators return a unique consistent base which represents the beliefs of the group. The operators, parameterized by a distance between interpretations and aggregation function, usually only take into account consistent bases, consequently some information which is not responsible for conflicts may be ignored. An alternative way of merging uses the notion of Partial Satisfiability to define -, a model-based merging operator that produces similar results to other merging approaches, but while other approaches require many merging operators in order to achieve satisfactory results for different scenarios - obtains similar results for all these different scenarios with a unique operator. This paper analyzes some of the properties satisfied by -.

Previous work on context-specific independence in Bayesian networks is driven by a common goal, namely to represent the conditional probability tables in a most compact way. In this paper, we argue from the view point of the knowledge compilation map and conclude that the language of Ordered Binary Decision Diagrams (OBDD) is the most suitable one for representing probability tables, in addition to the language of Algebraic Decision Diagrams (ADD). We thus suggest the replacement of the current practice of using tree-based or rule-based representations. This holds not only for inference in Bayesian networks, but is more generally applicable in the generic framework of semiring valuation algebras, which can be applied to solve a variety of inference and optimization problems in different domains.

This paper describes a new method for solving optimization queries in semiring valuation systems. In contrast to existing techniques which focus essentially on the identification of solution configurations, we propose foremost the construction of an implicit representation of the solution configuration set in the shape of a Boolean function. This intermediate compilation step allows then to efficiently execute many further relevant queries that go far beyond the traditional task of enumerating solution configurations.

Update of knowledge bases is becoming an important topic in Artificial Intelligence and a key problem in knowledge representation and reasoning. One of the latest ideas to update logic programs is choosing between models of Minimal Generalised Answer Sets to overcome disadvantages of previous approaches. This paper describes an implementation of the declarative version of updates sequences that has been proposed as an alternative to syntax-based semantics. One of the main contributions of this implementation is to use ’s Weak Constraints to compute the model(s) of an update sequence, besides presenting the precise definitions proposed by the authors and an online solver. As a result, the paper makes an outline of the basic structure of the system, describes the employed technology, discusses the major process of computing the models, and illustrates the system through examples.

In this paper some results are obtained regarding the existence and reachability of minimal fixed points for multiple-valued functions on a multilattice. The concept of inf-preserving multi-valued function is introduced, and shown to be a sufficient condition for the existence of minimal fixed point; then, we identify a sufficient condition granting that the immediate consequence operator for multilattice-based fuzzy logic programs is sup-preserving and, hence, computes minimal models in at most iterations.

In case intelligent agents get new knowledge and this knowledge must be added or updated to their knowledge base, it is important to avoid inconsistencies. Currently there are several approaches dealing with updates. In this paper, we propose a semantics for update sequences. We start introducing the notion of minimal generalized (MG) pstable models that, as we argue is interesting by itself. Based on MG pstable models we construct our update semantics. In this work, we also use some representative examples to compare our update semantics to other known update semantics and observe some advantages of it.

Uncertain information is present in many real applications medical domain, weather forecast, . The most common approaches for leading with this information are based on probability however some times; it is difficult to find suitable probabilities about some events. In this paper, we present a possibilistic logic programming approach which is based on possibilistic logic and PStable semantics. Possibilistic logic is a logic of uncertainty tailored for reasoning under incomplete evidence and Pstable Semantics is a solid semantics which emerges from the fusion of non-monotonic reasoning and logic programming; moreover it is able to express answer set semantics, and has strong connections with paraconsistent logics.

This paper is a contribution to improving computational efficiency of definite Prolog programs using Unfold/Fold (U/F) strategy with homeomorphic embedding as a control heuristic. Unfold/Fold strategy is an alternative to so called conjunctive partial deduction (CPD). The ECCE system is one of the best system for program transformations based on CPD. In this paper is presented a new fully automated system of program transformations based on U/F strategy. The experimental results, namely CPU times, the number of inferences, and the size of the transformed programs are included. These results are compared to the ECCE system and indicate that in many cases both systems have produced programs with similar or complementary efficiency.

Moreover, a new method based on a simple combination of both systems is presented. This combination represents, to our best knowledge, the most effective transformation program for definite logic programs. In most cases, the combination significantly exceeds both the Unfold/Fold algorithm presented here and the results of the ECCE system. The experimental results with a complete comparison among these algorithms are included.

Many regularity properties of strings, like those appearing in hardware specification and verification, can be expressed in terms of word equations. The solvability problem of word equations is NP-hard and the first algorithm to find a solution for a word equation, when this solution exists, was given by Makanin in 1977. The time complexity of Makanin’s algorithm is triple exponential in the length of the equations. In this paper we present an evolutionary algorithm with a local search procedure that is efficient for solving word equation systems. The fitness function of our algorithm is based on Levensthein distance considered as metric for the set of 0-1 binary strings. Our experimental results evidence that this metric is better suited for solving word equations than other edit metrics like, for instance, Hamming distance.