Showing papers in "Artificial Intelligence in 1974"

TL;DR: Examples of the ABSTRIPS system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides, and some further implications of the hierarchical planning approach are explored.

TL;DR: A solution to this problem has been implemented at Stanford using a calibrated camera model which expresses the relation between object space and image space as a function of the computer's control variables.

TL;DR: Optimal algorithms are derived for satisficing problem-solving search, that is, search where the goal is to reach any solution, no distinction being made among different solutions.

TL;DR: Mathematical decision theory can be combined with heuristic techniques to attack Artificial Intelligence problems and Bayesian decision theory is seen to provide a mechanism for including problem dependent (semantic) information in a general system.

TL;DR: This paper introduces a new restriction upon the heuristic, called the “bandwidth” condition, that enables the ordered search to better cope with time and space difficulties, and provides some additional insight to the general problem of searching game trees.

TL;DR: A man-machine theorem proving system at the University of Texas (Austin) which has been used to prove a few theorems in general topology, built around a modified form of IMPLY, a natural-deduction-like theorem proving technique.

TL;DR: A theorem prover that embodies knowledge about programming constructs, such as numbers, arrays, lists, and expressions, and is used as part of a program verification system that uses the Floyd-Naur explication of program semantics.

TL;DR: In this article, the authors present and discuss examples of mechanical inference problems which must be solved in order to construct effective mechanical speech understanding systems, which are taken from incremental simulations of a prototype speech understanding system which will use syntactic, semantic, and pragmatic information as well as acoustical and phonological information to mechanically understand continuous speech utterances.

TL;DR: The key ideas of this project are major new additions to a theory of representation of knowledge built on an inductive predicate logic.

TL;DR: Overall, the analyses demonstrated the viability of the analytic method, and provide further evidence in support of the competence theory [16] on which the analyses were based.

TL;DR: Two new machine learning procedures used to arrive at “knowledgeable” static evaluators for checker board positions are presented and are found to perform about equally well, despite the relative simplicity of the second.

TL;DR: In this article, an adaptive control procedure is described which is intended to improve both acoustic analysis and linguistic decoding in automatic recognition of continuous speech by bringing into agreement data available at each of these stages.

TL;DR: The method employed is to classify the intersections of drawings into various types and to postulate various possible configurations of objects and relations which could exist at each intersection, the descriptive procedure being to verify these hypotheses.