Nilsson, N. J.
"Because many artificial intelligence applications require the ability to reason with uncertain knowledge, it is important to seek appropriate generalizations of logic for that case. We present here a semantical generalization of logic in which the truth values of sentences are probability values (between 0 and 1). Our generalization applies to any logical system for which the consistency of a finite set of sentences can be established. The method described in the present paper combines logic with probability theory in such a way that probabilistic logical entailment reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1."See also: Probabilistic logic revisited, in Artificial Intelligence in Perspective, edited by Daniel Gureasko Bobrow, MIT Press, 1994.Artificial Intelligence, 28 (1), 71-87
Nilsson, N. J.
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Palo Alto, California: Tioga
"Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We describe a rule-based system that uses a partitioned semantic network representation for the premises and conclusions." In Waterman and Hayes-Roth, 203-221.
Nilsson, N. J.
"Problem-solving methods using some sort of heurstically guided search process have been the subject of much research in Artificial Intelligence. This paper groups these problem-solving methods under three major headings: the State-Space Approach, the Problem-Reduction Approach and the Formal-Logic Approach." New York: McGraw-Hill.
"Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies."See also: Correction IEEE Transactions on Systems Science and Cybernetics, SSC-4 (2), 100-107.