Probabilistic logic

Classics

"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


Controlling recursive inference

Classics

"Loosely speaking, recursive inference occurs when an inference procedure generates an infinite sequence of similar subgoals. In general, the control of recursive inference involves demonstrating that recursive portions of a search space will not contribute any new answers to the problem beyond a certain level. We first review a well-known syntactic method for controlling repeating inference (inference where the conjuncts processed are instances of their ancestors), provide a proof that it is correct, and discuss the conditions under which the strategy is optimal. We also derive more powerful pruning theorems for cases involving transitivity axioms and cases involving subsumed subgoals. The treatment of repeating inference is followed by consideration of the more difficult problem of recursive inference that does not repeat. Here we show how knowledge of the properties of the relations involved and knowledge about the contents of the system's database can be used to prove that portions of a search space will not contribute any new answers." Artificial Intelligence, 30 (3), 343-89.





Quantifying the Inductive Bias in Concept Learning

Classics

See also: 2005 AAAI Classic Paper Awards summary of significance by Tom Mitchell in AI Magazine 26(4), 2005.Proc. AAAI-86



Induction of decision trees

Classics

The technology for building knowledge-based systems by inductive inference from examples hasbeen demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directionsMachine Learning, 1, p. 81-106



Universal Subgoaling and Chunking: The Automatic Generation and Learning of Goal Hierarchies

Classics

"Chunking was first proposed as a model of human memory by Miller (1956), and has since become a major component of theories of cognition. More recently it has been proposed that a theory of human learning based on chunking ..."Kluwer Academic