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.

A given representational language can be implemented in all manner of ways: predicate calculus assertions may be implemented as lists, as character sequences, Minsky introduced the terminology of'frames' to unify and denote a loose as trees, as networks, as patterns in an associative memory, etc: collection of related ideas on knowledge representation: a collection which, all giving different computational properties but all encoding the same representational since the publication of his paper (Minsky, 1975) has become even looser.

It concerns the difficulty of keeping obey the extension property: If S p and S C S' then S' p.

The paper exhibits experience, and that of others using QA2 and QA3, solutions to these problems obtained by QA3, a one of the first difficulties encountered is the computer program based on these theorem-proving representation of problems, especially statetransformation methods.

The next step is to find an algorithm for finding paths in P2, then apply this al!drithin in a certain way as a heuristic Many combinatorially large problems cannot be solved for P1. As an elementary example, the rectilinear distance feasibly by exhaustive case analysis or brute force function is an efficient heuristic for finding paths in a search, but can be solved efficiently if a heuristic can be "Manhattan street pattern" graph even when some (but devised to guide the search. Finding such a heuristic for not too many) of the streets have been blockaded (i.e., a given problem, however, usually requires an exercise of some edges are removed from the. graph).

In this paper we describe some major new additions to the STRIPS robot problem-solving Before getting into details (and defining just what we mean by generalize), system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.

"Almost all the basic methods used by

I want to review what 1 consider to be the important results of this paper. One is the observation that, when we reason, we use representations of the objects 11.2. Concluding remarks, history and thanks we are reasoning about as well as a representation of the facts about these objects.

ABSTRACT Prospector is a computer consultant system intended to aid geologists in evaluating the favorability of an exploration site or region for occurrences of ore deposits of particular types. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof. We describe the form of models in Prospector, focussing on inference networks of geological assertions and the Bayesian propagation formalism used to represent the judgmental reasoning process of the economic geologist who serves as model designer. Following the initial design of a model, simple performance evaluation techniques are used to assess the extent to which the performance of the model reflects faithfully the intent of the model designer. These results identify specific portions of the model that might benefit from "fine tuning", and establish priorities for such revisions. This description of the Prospector system and the model design process serves to illustrate the process of transferring human expertise about a subjective domain into a mechanical realization. I. INTRODUCTION In an increasingly complex and specialized world, human expertise about diverse subjects spanning scientific, economic, social, and political issues plays an increasingly important role in the functioning of all kinds of organizations. Although computers have become indispensable tools in many endeavors, we continue to rely heavily on the human expert's ability to identify and synthesize diverse factors, to form judgments, evaluate alternatives, and make decisions -- in sum, to apply his or her years of experience to the problem at hand. This is especially valid with regard to domains that are not easily amenable to precise scientific formulations, i.e., to domains in which experience and subjective judgment plays a major role.

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.