Showing papers in "Artificial Intelligence in 1979"

TL;DR: The need of problem solvers to choose between alternative systems of beliefs is stressed, and a mechanism by which a problem solver can employ rules guiding choices of what to believe, what to want, and what to do is outlined.

TL;DR: The concept of meta-level knowledge is described and illustrations given of its utility in knowledge acquisition and its contribution to the more general issues of creating an intelligent program.

TL;DR: This paper attempts to outline one possible solution to controlling inferences, namely following what is interesting and ignoring what is not, in the context of artificial intelligence.

TL;DR: The algorithm, which is named B*, finds a proof that an arc at the root of a search tree is better than any other by attempting to find both the best arc atThe root and the simplest proof, in best-first fashion.

TL;DR: An algorithm based on state space search is introduced for computing the minimax value of game trees and the new algorithm SSS∗ is shown to be more efficient than α-s in the sense that SSS ∗ never evaluates a node thatα-s can ignore.

TL;DR: A knowledge-guided system for the analysis of range data about three-dimensional scenes with planar and/or curved objects is presented and a "junction dictionary" is constructed to represent the general knowledge about the physical nature of the three- dimensional objects world.

TL;DR: A method to obtain rewriting rules from the graph is described, and it is shown that these rewriting rules can be used to generate a refutation plan that may correspond to a large number of linear resolution refutations.

TL;DR: Several methods of inductive reasoning in the domain of recursive equations are investigated, including the method of generalization with beliefs, the methods of successive refinement, and temporal methods based on comparisons with previously solved problems.