Showing papers on "Artificial intelligence, situated approach published in 1978"

Artificial Intelligence and Learning Strategies.

Abstract: Publisher Summary This chapter describes the different kinds of knowledge and strategies necessary for understanding in three radically different domains, namely, stories, solutions to mathematical problems, and electronic circuits. The field of artificial intelligence grew out of the attempt in the late 1950s to build computer programs that could carry out tasks requiring human intelligence. The goal was to build machines that could understand language, recognize objects in scenes, act as intelligent robots, solve problems, play games such as chess, teach students about different subjects, etc. To build these programs, artificial intelligence has developed a variety of formalisms that in turn provide a new basis for analyzing cognitive processes. These formalisms are used to express structural and procedural mechanisms and theories about human problem-solving, planning, representing knowledge and understanding text by computers. A curriculum might teach the knowledge and strategies in a content-independent form, and then show how they apply to different content areas. Either approach would help the student to acquire more readily an understanding of a particular domain of knowledge. Transferring these skills would also have a significant effect on students' ability to acquire other quite separate domains of knowledge.

Trends in Artificial Intelligence.

Abstract: This paper discusses the foundations of Artificial Intelligence as a science and the types of answer that may be given to the question, “What is intelligence?” It goes on to compare the paradigms of Artificial Intelligence and general systems theory, and suggests that the links of general systems theory are closer to “brain science” than they are to Artificial Intelligence.

Artificial intelligence and empirical psychology

Abstract: Experiments with an AI program might be empirical in both senses The y could reveal a failure of the program to understand something it was intended to be able to cope with (a formal empirical discovery) or they could show that people sometimes use language in a f ashion not pre viously noticed (a substanti ve empirical discovery) Similarly, a vision program may fail where it w as intended to cope, or it may fail in tasks the programmer had not realised most people could cope with

Designing C.A.I. on Some Ideas of Artificial Intelligence

Abstract: While the application of ideas of artificial intelligence in C.A.I. has been in the structuring of information storage and retrieval, this paper shows how modifications of the concept of ‘proof’ studied in mathematical logic lead to a dialectical question answering system, much more similar to the Socratic method of convincing than to drilling of programmed learning. The design of C.A.I. programs for management accounting and social science shows how an interdisciplinary approach is practicable, and how the learner is encouraged to search for the metatheoretical assumptions of social science.