Ewa Orlowska - Böcker

Relational structures abound in our daily environment: relational databases, data mining, scaling procedures, preference relations, etc. As the documentation of scientific results achieved within the European COST Action 274, TARSKI, this book advances the understanding of relational structures and the use of relational methods in various application fields.The 12 revised full papers were carefully reviewed and selected for presentations. The papers are devoted to mechanization of relational reasoning, relational scaling and preferences, and algebraic and logical foundations of real world relations.

This monograph presents a systematic, exhaustive and up-to-date overview of formal methods and theories for data analysis and inference inspired by the concept of rough set. The book studies structures with incomplete information from the logical, algebraic and computational perspective. The formalisms developed are non-invasive in that only the actual information is needed in the process of analysis without external sources of information being required. The book is intended for researchers, lecturers and graduate students who wish to get acquainted with the rough set style approach to information systems with incomplete information.

This book is a follow-up of LNCS volume 2929 with the same title, and presents the major results of COST action 274 (2002-2005), TARSKI: Theory and - plications of Relational Structures as Knowledge Instruments. Relational structures abound in the daily environment: relational databases, data-mining, scaling procedures, preference relations, etc. Reasoning about, and with, relations has a long-standing European tradition, which may be divided into three broad areas: 1. Algebraic Logic: algebras of relations, relational semantics, and algebras and logics derived from information systems. 2. Computational Aspects of Automated Relational Reasoning: decidability and complexity of algorithms, network satisfaction. 3. Applications: social choice, AI, linguistics, psychology, economics, etc. The main objective of the ?rst TARSKI book (LNCS 2929) was to advance the understanding of relational structures and the use of relational methods in applicable object domains. There were the following sub-objectives: 1. Tostudythesemanticalandsyntacticalaspectsofrelationalstructuresarising from ‘real world’ situations 2. To investigateautomatedinference for relationalsystems, and, wherepossible or feasible, develop deductive systems which can be implemented into industrial applications, such as diagnostic systems 3. To develop non-invasive scaling methods for predicting relational data 4. To make software for dealing with relational systems commonly available We are con?dent that the present book will further the understanding of int- disciplinary issues involving relational reasoning. This book consists of papers which give a clear and self-contained overview of the results obtained by the TARSKI action, typically obtained by di?erent persons from di?erent work - eas.

This volume of the Transactions on Rough Sets commemorates the life and work of Zdzislaw Pawlak (1926-2006), whose legacy is rich and varied. It presents papers that reflect the profound influence of a number of research initiatives by Professor Pawlak, introducing a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science.

Together with volume VI of the Transactions on Rough Sets series, this book commemorates the life and work of Zdzislaw Pawlak (1926-2006). It presents papers that reflect the profound influence of a number of research initiatives by Professor Pawlak, introducing a number of advances in the foundations and applications of AI, engineering, logic, mathematics, and science, which have had significant implications in a number of research areas.

The construction of any broadly understood theory of information or infor­ mation processing system involves two major methodological processes: (1) abstraction and analysis, (2) reasoning and computing. This monograph is a realisation of these two processes in relation to the study of incompleteness of information. The paradigm we are working with is inspired by a rough-set approach to data analysis: the formalisms we develop enable the use of a non­ invasive data representation. This means that the only information which is and must be used in the process of analysis is the actual information that is to be analysed; we do not require any additional sources of information. An abstraction is formed in the process of conception, design, and develop­ ment of structures. Then analysis leads to a selection of a class of structures. In this book we delineate a class of informational structures that enable us to represent both numerical and non-numerical information and we analyse var­ ious manifestations of its incompleteness. We discuss several general types of incompleteness of information which are grounded in a rough-set-style view of imprecision and uncertainty. Manifestations of these types of incompleteness in information systems are investigated.

The origins of relational theories can be found in the work of three 19th cen- tury mathematicians: Augustus de Morgan (1864, On the syllogism IV and on the logic of relations), Charles Sanders Peirce (1882, Brief description of the algebra of relatives) and Ernst Schroder (1895, Vorlesungen iiber die Al- gebra und Logik der Relative). The modern origins of the theory of relations are due to Alfred Tarski (14 January 1902, Warsaw -26 October 1983, Berke- ley). His paper' On the calculus of Relations' published in 1941 gave rise to an algebraic theory of relations which is still extensively studied. In the 1970s, the applications of relational theories to various applied sciences emerged. Nowadays relational theories are experiencing a period of extensive development, with the emergence of new theories and systems allow- ing better understanding and better use of such theories.Relational theories have been used, among others, in the following fields: * Theory of programs: program specification, program verification, mod- elling concurrency, process calculi, semantics of programming languages; * Databases: relational databases, tabular methods, dependency theory, rectangular and difunctional decomposition of databases; * Computational linguistics: relational semantics of natural languages, re- lational grammars, Lambek calculus; * Spatial reasoning: modelling of relationships between space regions; * Handling uncertainty: fuzzy relations, many-valued relations, information relations. Indeed, the concept of relation emerges again and again throughout computer science, from its theoretical foundations to very practical implementations.

The book presents rough set formalisms and methods of modeling and handling incomplete information and motivates their applicability to knowledge representation, knowledge discovery and machine learning. The book focuses on providing representational and inference mechanisms for dealing with two particular aspects of incompleteness, namely indiscernibility and similarity. Those manifestations of particular aspects of incompleteness are inherent in any data structure and any cognitive unit. Knowledge discovered from such an information is uncertain in that it can only be asserted with a tolerance. The methods developed in the book are capable of exposing the limits of that tolerance and of making reliable inferences in the environments where complete information is not available.

This volume represents the state of the art for much current research in many-valued logics. Primary researchers in the field are among the authors. Major methodological issues of many-valued logics are treated, as well as applications of many-valued logics to reasoning with fuzzy information. Areas covered include: Algebras of multiple valued logics and their applications, proof theory and automated deduction in multiple valued logics, fuzzy logics and their applications, and multiple valued logics for control theory and rational belief.

The book presents rough set formalisms and methods of modeling and handling incomplete information and motivates their applicability to knowledge representation, knowledge discovery and machine learning. The book focuses on providing representational and inference mechanisms for dealing with two particular aspects of incompleteness, namely indiscernibility and similarity. Those manifestations of particular aspects of incompleteness are inherent in any data structure and any cognitive unit. Knowledge discovered from such an information is uncertain in that it can only be asserted with a tolerance. The methods developed in the book are capable of exposing the limits of that tolerance and of making reliable inferences in the environments where complete information is not available.

This volume represents the state of the art for much current research in many-valued logics. Primary researchers in the field are among the authors. Major methodological issues of many-valued logics are treated, as well as applications of many-valued logics to reasoning with fuzzy information. Areas covered include: Algebras of multiple valued logics and their applications, proof theory and automated deduction in multiple valued logics, fuzzy logics and their applications, and multiple valued logics for control theory and rational belief.

This book presents logical foundations of dual tableaux together with a number of their applications both to logics traditionally dealt with in mathematics and philosophy (such as modal, intuitionistic, relevant, and many-valued logics) and to various applied theories of computational logic (such as temporal reasoning, spatial reasoning, fuzzy-set-based reasoning, rough-set-based reasoning, order-of magnitude reasoning, reasoning about programs, threshold logics, logics of conditional decisions). The distinguishing feature of most of these applications is that the corresponding dual tableaux are built in a relational language which provides useful means of presentation of the theories. In this way modularity of dual tableaux is ensured. We do not need to develop and implement each dual tableau from scratch, we should only extend the relational core common to many theories with the rules specific for a particular theory.