A Comparative study of four major knowledge representation techniques used in expert systems with an implementation in Prolog

Knowledge representation is a central issue in Artifical Intelligence (AI) research. In order to solve the diverse and complex problems encountered, one needs both a large amount of knowledge and some mechanism for the management and skillful utilization of that knowledge. The basic problem in knowledge representation is the development of an adequate formalism to represent that knowledge. In this thesis I will discuss four of the major techniques for representing knowledge in expert systems: first order logic, production rules, semantic networks, and frames. Using Prolog as the implementation language, I will demonstrate that all of the above mentioned representation techniques, when used in actual implementations, will be reduced to an equivalency - that being a set of Prolog facts and rules. Prolog limits us to a set of facts expressed as predicate(argumentl, argument, ..., argumentn) and IF ... THEN rules, thus eliminating many of the unique features which characterize the various representation techniques. Therefore, Prolog can be viewed as a representation technique itself

Representing the Process of Machine Tool Calibration in First-order Logic

Machine tool calibration requires a wide range of measurement techniques that can be carried out in many different sequences. Planning a machine tool calibration is typically performed by a subject expert with a great understanding of International standards and industrial best-practice guides. However, it is often the case that the planned sequence of measurements is not the optimal. Therefore, in an attempt to improve the process, intelligent computing methods can be designed for plan suggestion. As a starting point, this paper presents a way of converting expert knowledge into first-order logic that can be expressed in the PROLOG language. It then shows how queries can be executed against the logic to construct a knowledge-base of all the different measurements that can be performed during machine tool calibration

Logic Programming for Finding Models in the Logics of Knowledge and its Applications: A Case Study

The logics of knowledge are modal logics that have been shown to be effective in representing and reasoning about knowledge in multi-agent domains. Relatively few computational frameworks for dealing with computation of models and useful transformations in logics of knowledge (e.g., to support multi-agent planning with knowledge actions and degrees of visibility) have been proposed. This paper explores the use of logic programming (LP) to encode interesting forms of logics of knowledge and compute Kripke models. The LP modeling is expanded with useful operators on Kripke structures, to support multi-agent planning in the presence of both world-altering and knowledge actions. This results in the first ever implementation of a planner for this type of complex multi-agent domains.Comment: 16 pages, 1 figure, International Conference on Logic Programming 201

On the Implementation of the Probabilistic Logic Programming Language ProbLog

The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming (TPLP

Description of GADEL

This article describes the first implementation of the GADEL system : a Genetic Algorithm for Default Logic. The goal of GADEL is to compute extensions in Reiter's default logic. It accepts every kind of finite propositional default theories and is based on evolutionary principles of Genetic Algorithms. Its first experimental results on certain instances of the problem show that this new approach of the problem can be successful.Comment: System Descriptions and Demonstrations at Nonmonotonic Reasoning Workshop, 2000 6 pages, 2 figures, 5 table

Attempto - From Specifications in Controlled Natural Language towards Executable Specifications

Deriving formal specifications from informal requirements is difficult since one has to take into account the disparate conceptual worlds of the application domain and of software development. To bridge the conceptual gap we propose controlled natural language as a textual view on formal specifications in logic. The specification language Attempto Controlled English (ACE) is a subset of natural language that can be accurately and efficiently processed by a computer, but is expressive enough to allow natural usage. The Attempto system translates specifications in ACE into discourse representation structures and into Prolog. The resulting knowledge base can be queried in ACE for verification, and it can be executed for simulation, prototyping and validation of the specification.Comment: 15 pages, compressed, uuencoded Postscript, to be presented at EMISA Workshop 'Naturlichsprachlicher Entwurf von Informationssystemen - Grundlagen, Methoden, Werkzeuge, Anwendungen', May 28-30, 1996, Ev. Akademie Tutzin