Artificial intelligence tasks which can be formulated as constraint satisfaction problems, with which this paper is for the most part concerned, are usually solved by backtracking.

In this section, I propose another, quite different view about the nature To choose their actions, reasoning programs must be able to make assumptions and subsequently of reasoning. I incorporate some new concepts into this view, and the combination revise their beliefs when discoveries contradict these assumptions. The Truth Maintenance System overcomes the problems exhibited by the conventional view.

This talk reviews those efforts in automatic theorem proving, during the past few years, which have theory, very easy for the computer.

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) .

For In this paper we discuss a general approach to detecting instance, a single Supreme Court case created the conceptual change developed in our on-going work on "automobile exception" to the Fourth Amendment's warrant concept drift [Rissland et al., 1994]. We illustrate our requirement for a constitutionally acceptable search; this approach on an actual, still evolving, legal example, the case forever changed the meaning of our Fourth "good faith" concept in the law of personal bankruptcy. In Amendment, which is still evolving today [Rissland, 1989; our approach, we detect that a concept is changing by Rissland & Collins, 19861.

In this paper, we consider a distinctly bottom-up which generates arguments by performing a heuristic approach.

J. Do RAN 433 22 Chromosome classification and segmentation as exercises in knowing what to expect.

B.RANDELL 3 PROGRAM PROOF AND MANIPULATION 2 Some techniques for proving correctness of programs which alter data structures.

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.

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.