Branch and Bound (B&B) is a problem-solving technique which is widely used for various problems encountered in operations research and combinatorial mathematics. Various heuristic search procedures used in artificial intelligence (AI) are considered to be related to B&B procedures. However, in the absence of any generally accepted terminology for B&B procedures, there have been widely differing opinions regarding the relationships between these procedures and B&B. This paper presents a formulation of B&B general enough to include previous formulations as special cases, and shows how two well-known AI search procedures (A∗ and AO∗) are special cases of this general formulation.

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.

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.

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.

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.

The basic thesis put forth in this article is that a problem solver is essentially an interpreter that carries out computations implicit in the problem formulation. A good problem formulation gives rise to what is conventionally called a strong problem solver; poor formulations correspond to weak problem solvers. Knowledge-based systems are discussed in the context of this thesis. We also make observations about the relationship between search strategy and problem formulation.

The basic thesis put forth in this article is that a problem solver is essentially an interpreter that carries out computations implicit in the problem formulation. A good problem formulation gives rise to what is conventionally called a strong problem solver; poor formulations correspond to weak problem solvers. Knowledge-based systems are discussed in the context of this thesis. We also make observations about the relationship between search strategy and problem formulation.

The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.