Discusses a research project that uses artificial intelligence techniques to help teach programing. Describes principles and implementation of the LISP Intelligent Tutoring System (LISPITS). Explains how the artificial intelligence technique was developed and possible future research. (MVL)

Discusses artificial intelligence, its definitions, and potential applications. Provides listings of Logo and BASIC versions for programs along with REM statements needed to make modifications for use with Apple computers. (RT)

Provides background material on logic programing and presents PROLOG as a high-level artificial intelligence programing language that borrows its basic constructs from logic. Suggests the language is one which will help the educator to achieve various goals, particularly the promotion of problem solving ability. (MVL)

Recent technological developments in interfaces, programing philosophy, and artificial intelligence may invite the participation of women programers, who find a concrete, intuitive, and informal style of programing more congenial than the hierarchical, rule-driven style heretofore pervasive in computer culture. (DM)

The nature of thinking is the subject of this second part in a series which is examining various topics and issues related to the controversy of whether or not computers can think. Suggests that intelligence is the ability to develop general ideas and not the ability to apply those ideas. (JN)

The development of symbolic computer algebra designed to manipulate abstract mathematical expressions is discussed. The ability of this software to mimic the standard patterns of human problem solving represents a major advance toward "true" artificial intelligence. (MP)

Discusses whether machines can think in the same sense that humans think. Provides four conclusions based on axioms of differences between computer programs and human minds. Compares claims of strong and weak artificial intelligence. (YP)

Computers, chip technologies, networking, and artificial intelligence all need vast improvement just to take their place as tools for building new computers. Research efforts, progress, and needs in each of these areas are addressed. (JN)

Discussion of the history and development of artificial intelligence (AI) highlights a bibliography of introductory books on various aspects of AI, including AI programing; problem solving; automated reasoning; game playing; natural language; expert systems; machine learning; robotics and vision; critics of AI; and representative software. (LRW)

Discusses various aspects of artificial intelligence, focusing on three interrelated issues: (1) representation of knowledge, which is roughly the machine equivalent of human memory; (2) control and use of knowledge, which corresponds to human abilities in problem solving and planning; and (3) the acquisition of knowledge, or what humans call…

To break the bottleneck inherent in today's linear computer architectures, parallel schemes (which allow computers to perform multiple tasks at one time) are being devised. Several of these schemes are described. Dataflow devices, parallel number-crunchers, programing languages, and a device based on a neurological model are among the areas…

Discusses recent progress in artificial intelligence in such narrowly defined areas as medical and electronic diagnosis. Also discusses use of expert systems, man-machine communication problems, novel programing environments (including comments on LISP and LISP machines), and types of knowledge used (factual, heuristic, and meta-knowledge). (JN)

Annotates reference material on artificial intelligence, mostly at an introductory level, with applications to education and learning. Topics include: (1) programing languages; (2) expert systems; (3) language instruction; (4) tutoring systems; and (5) problem solving and reasoning. (JM)

Discusses the nature and current state of knowledge-based systems and expert systems. Describes an expert system from the viewpoints of a computer programmer and an applications expert. Addresses concerns related to materials selection and forecasts future developments in the teaching of materials engineering. (ML)

Describes kinds of results achieved by computer programs in artificial intelligence. Topics discussed include heuristic searches, artificial intelligence/psychology, planning program, backward chaining, learning (focusing on Winograd's blocks to explore learning strategies), concept learning, constraint propagation, language understanding…