This article presents a dual dependency between AI and programming methodologies. AI is an important source of ideas and tools for building sophisticated support facilities which make possible certain programming methodologies. These advanced programming methodologies in turn can have profound effects upon the methodology of AI research. Both of these dependencies are illustrated by the example of anew experimental programming methodology which is based upon current AI ideas about reasoning, representation and control. The manner in which AI systems are designed, developed and tested can be significantly improved in the programming is supported by a sufficiently powerful partial evaluator. In particular, the process of building levels of interpreters and of intertwining generate and test can be partially automated. Finally speculations about a more direct connection between AI and partial evaluation are presented.

How to Get the Most Out of IJCAI-83 Vol IV, No 1, p 35, Winter-Spring, 1983.

Artificial Intelligence, 23, 29–58.

"By studying the problem-solving techniques that people use to design algorithms we can learn something about building systems that automatically derive algorithms or assist human designers. In this paper we present a model of algorithm design based on our analysis of the protocols of two subjects designing three convex hull algorithms. The subjects work mainly in a data-flow problem space in which the objects are representations of partially specified algorithms. A small number of general-purpose operators construct and modify the representations; these operators are adapted to the current problem state by means-ends analysis. The problem space also includes knowledge-rich schemas such as divide and conquer that subjects incorporate into their algorithms. A particularly versatile problem-solving method in this problem space is symbolic execution, which can be used to refine, verify, or explain components of an algorithm. The subjects also work in a task-domain space about geometry. The interplay between problem solving in the two spaces makes possible the process of discovery. We have observed that the time a subject takes to design an algorithm is proportional to the number of components in the algorithm's data-flow representation. Finally, the details of the problem spaces provide a model for building a robust automated system." Information Processing and Management 20(l-2):97-118.

In keeping with a desire to abstract general principles in AI, this article begins to examine some relationships among heuristic learning in search, classification of utility, properties of certain structures, measurement of acquired knowledge, and efficiency of associated learning. In the process, a simple definition is given for conceptual knowledge, considered as information compression. The discussion concludes that domain-specific conceptual knowledge can be acquired. Among other implications of the analysis is that statistical observation of probabilities can result in the equivalent of planning, in low susceptibility to error, and in efficient learning.

In keeping with a desire to abstract general principles in AI, this article begins to examine some relationships among heuristic learning in search, classification of utility, properties of certain structures, measurement of acquired knowledge, and efficiency of associated learning. In the process, a simple definition is given for conceptual knowledge, considered as information compression. The discussion concludes that domain-specific conceptual knowledge can be acquired. Among other implications of the analysis is that statistical observation of probabilities can result in the equivalent of planning, in low susceptibility to error, and in efficient learning.

The subject the idea has changed psychology, anthropology, sociology, is attempting to make sense of the world, and often coping and psychiatry should make its pervasiveness and importance with incomplete information, failure to understand, or lacking more evident.

Artificial Intelligence, as a maturing scientific/engineering discipline, is beginning to find its niche among the variety of subjects that are relevant to intelligent, perceptive behavior. A view of AI is presented that is based on a declarative representation of knowledge with semantic attachments to problem-specific procedures and data structures. Several important challenges to this view are briefly discussed. It is argued that research in the field would be stimulated by a project to develop a computer individual that would have a continuing existence in time.

Cooperative distributed problem solving networks are distributed networks of semi-autonomous processing nodes that work together to solve a single problem. The Distributed Vehicle Monitoring Testbed is a flexible and fully-instrumented research tool for empirically evaluating alternative designs for these networks. The testbed simulates a class of a distributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. it implements a novel generic architecture for distributed problems solving networks that exploits the use of sophisticated local node control and meta-level control to improve global coherence in network problem solving; (2.)

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern AI systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. This article presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas. A historical survey outlining the development of various approaches to machine learning is presented from early neural networks to present knowledge-intensive techniques.