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.

TEntEsuis is a program designed to provide assistance on the task of building knowledge-based results and motivations for its actions.

A plan for a question required the composition of REQUEST and INFORM and led to the development of two new kinds of informing speech acts, INFORMREF To plan a yes/no question about some proposition P. one should think that the and INFORMIF, and their mediating acts. The INFORMREF acts lead to hearer knows whether P is true or false (or, at least "might know"). An approximate "what," "when," and "where" questions while INFORMIF results in a yes/no representation of AGT2's knowing whether P is true or false is OR (AGT2 question.2' The reason for these new acts is that, in planning a REQUEST that BELIEVE P, AGT2 BELIEVE -- P)).'9 Such goals are often created, as modelled someone else perform an INFORM act, one only has incomplete knowledge of by our type 4 inference, when a planner does not know the truth-value of P. their beliefs and goals; but an INFORM, as originally defined can only be Typical circumstances in which an agent may acquire such disjunctive beliefs planned when one knows what is to be said.

Another concern has been to exploit (d) detection of metabolic disorders of genetic, developmental, toxic or infectious the AI methodology to understand better some fundamental questions in the origins by identification of organic constituents excreted in abnormal quantities philosophy of science, for example the processes by which explanatory hypotheses in human body fluids.

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