Foundation of logic historically dates back to the times of Aristotle, who pioneered the concept of truth/falsehood paradigm in reasoning. Mathematical logic of propositions and predicates, which are based on the classical models of Aristotle, underwent a dramatic evolution during the last 50 years for its increasing applications in automated reasoning on digital computers. The subject of Logic Programming is concerned with automated reasoning with facts and knowledge to answer a user s query following the syntax and semantics of the logic of propositions/predicates. The credit of automated reasoning by logic programs goes to Professor Robinson for his well-known resolution theorem that provides a general scheme to select two program clauses for deriving an inference. Until now Robinson s theorem is being used in PROLOG/DATALOG compilers to automatically build a Select Linear Definite (SLD) clause based resolution tree for answering a user s query. The SLD-tree based scheme for reasoning undoubtedly opened a new era in logic programming for its simplicity in implementation in the compilers. In fact, SLD-tree construction suffices the need for users with a limited set of program clauses. But with increase in the number of program clauses, the execution time of the program also increases linearly by the SLD-tree based approach. An inspection of a large number of logic programs, however, reveals that more than one pair of program clauses can be resolved simultaneously without violating the syntax and the semantics of logic programming. This book employs this principle to speed up the execution time of logic programs."
Answer set programming (ASP) is a declarative language tailored towards solving combinatorial optimization problems. It has been successfully applied to e.g. planning problems, configuration and verification of software, diagnosis and database repairs. However, ASP is not directly suitable for modeling problems with continuous domains. Such problems occur naturally in diverse fields such as the design of gas and electricity networks, computer vision and investment portfolios. To overcome this problem we study FASP, a combination of ASP with fuzzy logic -- a class of manyvalued logics that can handle continuity. We specifically focus on the following issues: 1. An important question when modeling continuous optimization problems is how we should handle overconstrained problems, i.e. problems that have no solutions. In many cases we can opt to accept an imperfect solution, i.e. a solution that does not satisfy all the stated rules (constraints). However, this leads to the question: what imperfect solutions should we choose? We investigate this question and improve upon the state-of-the-art by proposing an approach based on aggregation functions. 2. Users of a programming language often want a rich language that is easy to model in. However, implementers and theoreticians prefer a small language that is easy to implement and reason about. We create a bridge between these two desires by proposing a small core language for FASP and by showing that this language is capable of expressing many of its common extensions such as constraints, monotonically decreasing functions, aggregators, S-implicators and classical negation. 3. A well-known technique for solving ASP consists of translating a program P to a propositional theory whose models exactly correspond to the answer sets of P. We show how this technique can be generalized to FASP, paving the way to implement efficient fuzzy answer set solvers that can take advantage of existing fuzzy reasoners.
Since I started working in the area of nonlinear programming and, later on, variational inequality problems, I have frequently been surprised to find that many algorithms, however scattered in numerous journals, monographs and books, and described rather differently, are closely related to each other. This book is meant to help the reader understand and relate algorithms to each other in some intuitive fashion, and represents, in this respect, a consolidation of the field. The framework of algorithms presented in this book is called Cost Approxi- mation. (The preface of the Ph.D. thesis [Pat93d] explains the background to the work that lead to the thesis, and ultimately to this book.) It describes, for a given formulation of a variational inequality or nonlinear programming problem, an algorithm by means of approximating mappings and problems, a principle for the update of the iteration points, and a merit function which guides and monitors the convergence of the algorithm. One purpose of this book is to offer this framework as an intuitively appeal- ing tool for describing an algorithm. One of the advantages of the framework, or any reasonable framework for that matter, is that two algorithms may be easily related and compared through its use. This framework is particular in that it covers a vast number of methods, while still being fairly detailed; the level of abstraction is in fact the same as that of the original problem statement.
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