Reactive preferential structures and nonmonotonic consequence

We introduce information bearing systems (IBRS) as an abstraction of many logical systems. We define a general semantics for IBRS, and show that IBRS generalize in a natural way preferential semantics and solve open representation problems

Cumulativity without closure of the domain under finite unions

For nonmonotonic logics, Cumulativity is an important rule. We show here that Cumulativity fans out into an infinity of different conditions, if the domain is not closed under finite unions

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

Computer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application.This work was supported in part by Projects CICYT TIN2008- 06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008–07029-C02–02.Publicad

Negation by default and unstratifiable logic programs

AbstractThe default approach to the theory of logic programs (and deductive databases) is based on the interpretation of negation by default rules. Default logic is a well-suited formalism to express the Closed World Assumption and to define the declarative semantics of stratifiable logic programs. The case of disjunctive consequences in rules is treated. General logic programs may not have a meaning with respect to default semantics. The contribution of the paper is to exhibit an interesting class of programs having a default semantics, called effectively stratifiable programs. This time, disjunctive consequences are not considered. Effective stratification is a weaker constraint than stratification, local stratification and weak stratification. Besides enlarging the class of stratifiable logic programs, the paper contributes to provide a constructive definition of well-founded models of logic programs. The class of effectively stratifiable logic programs matches the class of programs having a total well-founded model and in general, the default semantics extends the well-founded semantics

General logical databases and programs: Default logic semantics and stratification

AbstractDefault logic is introduced as a well-suited formalism for defining the declarative semantics of deductive databases and logic programs. After presenting, in general, how to use default logic in order to define the meaning of logical databases and logic programs, the class of stratifiable databases and programs is extensively studied in this framework. Finally, the default logic approach to the declarative semantics of logical databases and programs is compared with the other major approaches. This comparison leads to showing some advantages of the default logic approach

Perspectives in deductive databases

AbstractI discuss my experiences, some of the work that I have done, and related work that influenced me, concerning deductive databases, over the last 30 years. I divide this time period into three roughly equal parts: 1957–1968, 1969–1978, 1979–present. For the first I describe how my interest started in deductive databases in 1957, at a time when the field of databases did not even exist. I describe work in the beginning years, leading to the start of deductive databases about 1968 with the work of Cordell Green and Bertram Raphael. The second period saw a great deal of work in theorem providing as well as the introduction of logic programming. The existence and importance of deductive databases as a formal and viable discipline received its impetus at a workshop held in Toulouse, France, in 1977, which culminated in the book Logic and Data Bases. The relationship of deductive databases and logic programming was recognized at that time. During the third period we have seen formal theories of databases come about as an outgrowth of that work, and the recognition that artificial intelligence and deductive databases are closely related, at least through the so-called expert database systems. I expect that the relationships between techniques from formal logic, databases, logic programming, and artificial intelligence will continue to be explored and the field of deductive databases will become a more prominent area of computer science in coming years

Symmetries in logic programs

We investigate the structures and above all, the applications of a class of symmetric groups induced by logic programs. After establishing the relationships between minimal models of logic programs and their simplified forms, and models of their completions, we show that in general when deriving negative information, we can apply the CWA, the GCWA, and the completion procedure directly from some simplified forms of the original logic programs. The least models and the results of SLD-resolution stay invariant for definite logic programs and their simplified forms. The results of SLDNF-resolution, the standard or perfect models stay invariant for hierarchical, stratified logic programs and some of their simplified forms, respectively. We introduce a new proposal to derive negative information termed OCWA, as well as the new concepts of quasi-definite, quasi-hierarchical and quasi-stratified logic programs. We also propose semantics for them

Disjunctive deductive databases.

by Hwang Hoi Yee Cothan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 68-70).Abstract --- p.iiAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives of the Thesis --- p.1Chapter 1.2 --- Overview of the Thesis --- p.7Chapter 2 --- Background and Related Work --- p.8Chapter 2.1 --- Deductive Databases --- p.8Chapter 2.2 --- Disjunctive Deductive Databases --- p.10Chapter 2.3 --- Model tree for disjunctive deductive databases --- p.11Chapter 3 --- Preliminary --- p.13Chapter 3.1 --- Disjunctive Logic Program --- p.13Chapter 3.2 --- Data-disjunctive Logic Program --- p.14Chapter 4 --- Semantics of Data-disjunctive Logic Program --- p.17Chapter 4.1 --- Model-theoretic semantics --- p.17Chapter 4.2 --- Fixpoint semantics --- p.20Chapter 4.2.1 --- Fixpoint operators corresponding to the MMSpDD --- p.22Chapter 4.2.2 --- "Fixpoint operator corresponding to the contingency model, CMP" --- p.25Chapter 4.3 --- Equivalence between the model-theoretic and fixpoint semantics --- p.26Chapter 4.4 --- Operational Semantics --- p.30Chapter 4.5 --- Correspondence with the I-table --- p.31Chapter 5 --- Disjunctive Deductive Databases --- p.33Chapter 5.1 --- Disjunctions in deductive databases --- p.33Chapter 5.2 --- Relation between predicates --- p.35Chapter 5.3 --- Transformation of Disjunctive Deductive Data-bases --- p.38Chapter 5.4 --- Query answering for Disjunctive Deductive Data-bases --- p.40Chapter 6 --- Magic for Data-disjunctive Deductive Database --- p.44Chapter 6.1 --- Magic for Relevant Answer Set --- p.44Chapter 6.1.1 --- Rule rewriting algorithm --- p.46Chapter 6.1.2 --- Bottom-up evaluation --- p.49Chapter 6.1.3 --- Examples --- p.49Chapter 6.1.4 --- Discussion on the rewriting algorithm --- p.52Chapter 6.2 --- Alternative algorithm for Traditional Answer Set --- p.54Chapter 6.2.1 --- Rule rewriting algorithm --- p.54Chapter 6.2.2 --- Examples --- p.55Chapter 6.3 --- Contingency answer set --- p.56Chapter 7 --- Experiments and Comparison --- p.57Chapter 7.1 --- Experimental Results --- p.57Chapter 7.1.1 --- Results for the Traditional answer set --- p.58Chapter 7.1.2 --- Results for the Relevant answer set --- p.61Chapter 7.2 --- Comparison with the evaluation method for Model tree --- p.63Chapter 8 --- Conclusions and Future Work --- p.66Bibliography --- p.6