Domain logic programming : theory and applications to scientific knowledge representation
نویسنده
چکیده
Scienti c Knowledge Representation deals with systems {real or abstract{ that are representable by mathematical models. The eld is very rich, encompassing problems from the realms of physics, engineering and economics, among others. These problems demand both mathematical knowledge of object or situation behaviour as well as knowledge about the applicability and interaction of these various behaviours. Traditional Arti cial Intelligence (AI) techniques stumble on the inability of existing AI tools to deal with mathematics. On the other hand the existing algebraic manipulation tools, although powerful enough in their mathematical capabilities, provide little means for encoding the non-mathematical knowledge occuring in the problems. We develop a variant of First Order Logic and an associated computational scheme suited for Scienti c Knowledge Representation. We call the scheme domain logic programming to re ect the central rôle of the mathematical domains of discourse. The scheme is an extension of Logic Programming (LP) in the spririt of Constraint Logic Programming (CLP). It allows direct algebraic manipulation of mathematical terms by means of special purpose solvers associated with each domain while still retaining model-theoretic semantics. Unlike CLP, minimal restrictions are imposed on the nature of the domains in order to support the wide range of mathematical domains required. Following on from a study of the structure of scienti c problems, the thesis presents the general Domain Logic Programming scheme. It then focuses on one particular instance, the programming language Euclid, which provides embedded mathematical knowledge of reals, intervals, vectors and real symbolic functions. A number of examples are used to demonstrate the suitability of Euclid for Scienti c Knowledge Representation. The examples are also used as a platform for comparison with the existing approaches and techniques.
منابع مشابه
Deep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملKnowledge Representation, Computation, and Learning in Higher-order Logic
This paper contains a systematic study of the foundations of knowledge representation, computation, and learning in higher-order logic. First, a polymorphically-typed higher-order logic, whose origins can be traced back to Church’s simple theory of types, is presented. A model theory and proof theory for this logic are developed and basic theorems relating these two are given. A metric space of...
متن کاملA Generalized Resolution Theorem
W.C. Rounds and G.-Q. Zhang have recently proposed to study a form of resolution on algebraic domains [1]. This framework allows reasoning with knowledge which is hierarchically structured and forms a (suitable) domain, more precisely, a coherent algebraic cpo as studied in domain theory. In this paper, we give conditions under which a resolution theorem — in a form underlying resolution-based ...
متن کاملMulti-dimensional Dynamic Knowledge Representation
According to Dynamic Logic Programming (DLP), knowledge may be given by a sequence of theories (encoded as logic programs) representing different states of knowledge. These may represent time (e.g. in updates), specificity (e.g. in taxonomies), strength of updating instance (e.g. in the legislative domain), hierarchical position of knowledge source (e.g. in organizations), etc. The mutual relat...
متن کاملAutomatic Knowledge Acquisition for Expert Systems
The purpose of this article is to summarize the state-of-the-art of the expert systems research field. First, we introduce the basic notion of knowledge, and specifically of shallow knowledge, and deep knowledge. The first section of the document summarizes the history of the field. We analyze the differences between the first generation of expert systems, based primarily upon rulebased and fra...
متن کامل