Image from the Lisp 1.5 Programmer's Manual.
Our own early attempts (as devoted users of LISP) to use PROLOG convinced us that it would be worth the effort to create within LISP a faithful implementation of Kowalski's logic programming idea. We felt it would be very convenient to be able to set up a knowledge base of assertions inside a LISP workspace, and to compute the answers to queries simply by executing appropriate function calls.
W3C Recommendation "This document describes the OWL Web Ontology Language. OWL is intended to be used when the information contained in documents needs to be processed by applications, as opposed to situations where the content only needs to be presented to humans. OWL can be used to explicitly represent the meaning of terms in vocabularies and the relationships between those terms. This representation of terms and their interrelationships is called an ontology."
"This paper discusses the POP-2 language and Multipop time-sharing system developed during the second half of the 1960s. POP-2's expressiveness spanned numeric and symbolic programming and supported experiments in logic of programming languages (by Boyer and Moore) and programming language concepts (for example, Michie's memoisation). The paper also discusses how the goals of POP-2 might be achieved in a successor language."
We briefly describe the logic programming language PROLOG concentrating on those aspects of the language that make it suitable for implementing expert systems. We show how features of expert systems such as:
(1) inference generated requests for data, (2) probabilistic reasoning, (3) explanation of behaviour
can be easily programmed in PROLOG. We illustrate each of these features by showing how a fault finder expert could be programmed in PROLOG.
PROLOG is a simple and powerful progamming language based on first-order logic. This paper examines two possible extensions to the language which would generally be considered "higher-order".t The first extension introduces lambda expressions and predicate variables so that functions and relations can be treated as 'first class' data objects. We argue that this extension does not add anything to the real power of the language. The other extension concerns the introduction of set expressions to denote the set of all (provable) solutions to some goal. We argue that this extension does indeed fill a real gap in the language, but must be defined with care.
"New directions in Artificial Intelligence research have led to the need for certain novel features to be embedded in programming languages. This paper gives an overview of the nature of these features, and their implementation in four principal families of AI languages: SAIL; PLANNER/CONNIVER; QLISP/INTERLISP; and POPLER/POP-2. The programming featurcs described include: new data types and accessing mechanisms for stored expressions; more flexible control structures, including multiple processes and backtracking; pattern matching to allow comparison of data item with a template, and extraction of labeled subexpressions; and deductive mechanisms which allow the programming system to carry out certain activities including modifying the data base and deciding which subroutines to run next using only constraints and guidelines set up by the programmer."
"Structure and Interpretation of Computer Programs has been MIT's introductory pre-professional computer science subject since 1981. It emphasizes the role of computer languages as vehicles for expressing knowledge and it presents basic principles of abstraction and modularity, together with essential techniques for designing and implementing computer languages. This course has had a worldwide impact on computer science curricula over the past two decades. ... These twenty video lectures by Hal Abelson and Gerald Jay Sussman are a complete presentation of the course, given in July 1986 for Hewlett-Packard employees, and professionally produced by Hewlett-Packard Television. The videos have been used extensively in corporate training at Hewlett-Packard and other companies, as well as at several universities and in MIT short courses for industry."
Loom is a language and environment for constructing intelligent applications. The heart of Loom is a knowledge representation system that is used to provide deductive support for the declarative portion of the Loom language. Declarative knowledge in Loom consists of definitions, rules, facts, and default rules. A deductive engine called a classifier utilizes forward-chaining, semantic unification and object-oriented truth maintainance technologies in order to compile the declarative knowledge into a network designed to efficiently support on-line deductive query processing.
Classic is a family of knowledge representation (KR) systems designed for applications where only limited expressive power is necessary, but rapid responses to questions are essential. The Classic systems are based on description logics (DLs), which gives them an object-centered flavor, and thus most of the features available in semantic networks are also available in Classic. Classic has a framework that allows users to represent descriptions, concepts, roles, individuals and rules. Classic allows for both primitive concepts, similar to the classes and frames of other knowledge representation systems and object-oriented programming languages, and defined concepts, i.e. concepts that have both necessary and sufficient conditions for membership.
This lesson will introduce you to objects, classes, inheritance, interfaces, and packages. Each discussion focuses on how these concepts relate to the real world, while simultaneously providing an introduction to the syntax of the Java programming language.
"One of the results of research in the area of artificial intelligence has been the development of techniques which allow the modeling of information at higher levels of abstraction. These techniques are embodied in languages or tools which allow programs to be built that closely resemble human logic in their implementation and are therefore easier to develop and maintain. These programs, which emulate human expertise in well defined problem domains, are called expert systems. The availability of expert system tools, such as CLIPS, has greatly reduced the effort and cost involved in developing an expert system. Rule-based programming is one of the most commonly used techniques for developing expert systems. In this programming paradigm, rules are used to represent heuristics, or 'rules of thumb,' which specify a set of actions to be performed for a given situation. A rule is composed of an if portion and a then portion. ... The origins of the C Language Integrated Production System (CLIPS) date back to 1984 at NASA's Johnson Space Center. ... CLIPS is now maintained independently from NASA as public domain software." - excerpt from, What is CLIPS?
Daniel Bobrow, Chairman of the Common LISP Object Specification Committee, characterized this lecture as "a snapshot of history in action." After discussing the history of object systems and the current state of the art, he explores the process of developing this new standard.
This paper presents a machine designed for compact representation and rapid execution of LISP programs. The machine language is a factor of 2 to 5 more compact than S-expressions or conventional compiled code, and the.compiler is extremely simple. The encoding scheme is potentiall y applicable to data as wel l as program. The machine also provides for user-defined data structures.