When you lose a game of chess to a computer then don't pretend that you didn't think at the game.

Bayesian inference nets 190 algorithms, structure-moving task 209-17 Kononenko's treatment 201-2 ALG(SIG, E) 50

Duce uses a set of transformations of propositional Horn clauses which successively compress the example material on the basis of generalizations and the addition of new terms. In the following descriptions of three of the six Duce operators, lower-case Greek letters stand for conjunctions of propositional symbols.

In this paper we will be concerned with such reasoning in its most general form, that is, in inferences that are defeasible: given more information, we may retract them. The purpose of this paper is to introduce a form of non-monotonic inference based on the notion of a partial model of the world. We take partial models to reflect our partial knowledge of the true state of affairs. We then define non-monotonic inference as the process of filling in unknown parts of the model with conjectures: statements that could turn out to be false, given more complete knowledge. To take a standard example from default reasoning: since most birds can fly, if Tweety is a bird it is reasonable to assume that she can fly, at least in the absence of any information to the contrary. We thus have some justification for filling in our partial picture of the world with this conjecture. If our knowledge includes the fact that Tweety is an ostrich, then no such justification exists, and the conjecture must be retracted.

A problem simplification approach that generates heuristics for constraint-satisfaction problems ' 125 R. DECHTER and J. PEARL 7. The relation between programming and specification languages with particular reference to Anna 157 A. D. MCGETTRICK and J. G. STELL LOGIC PROGRAMMING TOOLS AND APPLICATIONS 8. YAPES: yet another PROLOG expert system Decision trees and multi-valued attributes 305 J. R. QUINLAN 14. RuleFactory: a new inductive learning shell 319 S. RENNER 15.

It provides inference and explanation facilities, and incorporates a novel form of plausible inference. YAPES is a specialized interpreter for logic programs. Figure 1 illustrates its top level structure. A PROLOG interpreter (or compiler) executes such programs consisting of sets of Horn clauses, a form of first-order logic. The YAPES system also executes such programs, as well as programs in an extended version of Horn clause logic which uses certainties as truth values, rather than just true and false.

The initial plan for LOGLISP [1] was simply that it would offer, within LISP, a Horn-clause relational programming facility akin to PROLOG. This it does, but with some differences from PROLOG, notably the use of a breadth-first, rather than depth-first, elaboration of the underlying tree of alternative linear proofs, and the consequent avoidance of explicit backtracking as a control mechanism. It was because of these differences that the facility was called LOGIC rather than PROLOG, which would have been misleading. The name LOGLISP then refers to the combined system: LOGIC LISP. It soon became apparent, however, that the main interest of LOGLISP lay rather in its (relatively crude, but genuine) attempt to merge the functional programming style of LISP with the relational programming style of LOGIC and PROLOG. This was done by introducing the notion of'Lisp-transforms' into LOGIC.

It is a rich source of difficult and challenging problems which involve issues of knowledge representation, the analysis of natural language, and the automation of practical and common-sense reasoning.

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.