Catálogo de publicaciones - libros

This paper presents a formal method based on the high-level semantics of processes to reason about continuous change. With a case study we show how the semantics of processes can be integrated with the situation calculus. The soundness and completeness of situation calculus with respect to the process semantics are proven. Furthermore, the logical programming is implemented to support the semantics of processes with the situation calculus.

In this paper, we investigate the deficiency of Goyal and Egenhofer’s method for modeling cardinal directional relations between simple regions and provide the computational model based on the concept of mathematical morphology, which can be a complement and refinement of Goyal and Egenhofer’s model for crisp regions. Based on fuzzy set theory, we extend Goyal and Egenhofer’s model to handle fuzziness and provide a computational model based on alpha-morphology, which combines fuzzy set theory and mathematical morphology, to refine the fuzzy cardinal directional relations. Then the computational problems are investigated. We also give an example of spatial configuration in 2-dimensional discrete space. The experiment results confirm the cognitive plausibility of our computational models.

This paper presents a novel method for finding interesting patterns from spatio-temporal data. First, we perform a pairwise comparison of spatio-temporal sequences using the multiscale matching, taking into account the requirements for multiscale observation. Next, we construct the clusters of sequences using rough-set based clustering technique. Experimental results on real soccer game records demonstrated that the method could discover some interesting pass patterns that may be associated with successful goals.

We discuss the problems of spatio-temporal reasoning in the context of hierarchical information maps and approximate reasoning networks (AR networks). Hierarchical information maps are used for representation of domain knowledge about objects, their parts, and their dynamical changes. They are constructed out of information maps connected by some spatial relations. Each map describes changes (e.g., in time) of states corresponding to some parts of complex objects. We discuss the details of defining relations between levels of hierarchical information maps as well as between parts satisfying some additional constraints, e.g. spatial ones.

In this paper, we propose a logical framework for reasoning about uncertain belief fusion. The framework is a combination of multi-agent epistemic logic and possibilistic logic. We use graded epistemic operators to represent agents’ uncertain beliefs, and the operators are interpreted in accordance with possibilistic semantics. Ordered fusion can resolve the inconsistency caused by direct fusion. We consider two strategies to merge uncertain beliefs. In the first strategy, called level cutting fusion, if inconsistency occurs at some level, then all beliefs at the lower levels are discarded simultaneously. In the second, called level skipping fusion, only the level at which the inconsistency occurs is skipped. We present the formal semantics and axiomatic systems for these two strategies.

As an extension of the traditional modal logic, the fuzzy first-order modal logic is discussed in this paper. A description of fuzzy first-order modal logic based on constant domain semantics is given, and a formal system of fuzzy reasoning based on the semantic information of models of first-order modal logic is established. It is also introduced in this paper the notion of the satisfiability of the reasoning system and some properties associated with the satisfiability are proved.

In this paper, we propose arrow decision logic (ADL), which combines the main features of decision logic and arrow logic. Decision logic represents and reasons about knowledge extracted from decision tables based on rough set theory, while arrow logic is the basic modal logic of arrows. The semantic models of ADL are pairwise comparison tables, which are useful in rough set-based multicriteria analysis. Consequently, ADL can represent preference knowledge induced from multicriteria decision tables.

In different fields data are presented under the form of Property Systems or Attribute Systems (i. e. Information Systems). In order to collect items linked together by attributes or properties we can use a number of techniques whose results range from exact classifications to different kinds of approximations. This range depends on the collecting operators and the characteristics of the Information System at hand. In this paper we discuss how to transform Information Systems in order to apply a well-funded set of operators and to improve their precision.

In this paper, we introduce a discrete event control for Cat and Mouse example based on a paraconsistent logic program EVALPSN stable model computation. Predicting and avoiding control deadlock states are crucial problems in discrete event control systems. We show that the EVALPSN control can deal with prediction and avoidance of control dadlock states in the Cat and Mouse by defining general rules to represent the deadlock states in EVALPSN, and is much more flexible than the previous version of EVALPSN Cat and Mouse control. We also show how to translate the control properties of the Cat and Mouse into EVALPSN.

Granular computing as an enabling technology and as such it cuts across a broad spectrum of disciplines and becomes important to many areas of applications. In this paper, the notions of tolerance relation based information granular space are introduced and formalized mathematically. It is a uniform model to study problems in model recognition and machine learning. The key strength of the model is the capability of granulating knowledge in both consecutive and discrete attribute space based on tolerance relation. Such capability is reestablished in granulation and an application in information classification is illustrated. Simulation results show the model is effective and efficient.