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PROCEEDINGS AU ToMA T

AI Classics

Where the accountants have fallen down, however, is in their reluctance and sometimes inability to make intensive studies of different equipment and to specify their requirements for equipment. As one authority in the field of electronic data processing has pointed out, "Accountants, unlike engineers, take the equipment as given without bothering to specify their own particular needs." But after all things are taken into consideration, it is of primary importance that the personnel who are handling the details of the investigation have a good knowledge of the particular application to be studied. Executives in many companies have been dissatisfied with the help received from outsiders who are expert programmers and who know a lot about equipment, but who are unfamiliar with business systems. In some companies executives have found that their own personnel, who know the firm's particular data processing system, after three or four months of experience in which to grasp the logics of the computer and the intricacies of programming, are much more valuable than such outside experts.



Purposive Understanding

AI Classics

For the past ten years we have been working on the problem of getting a computer to understand natural language.



MACHINE INTELLIGENCE 11

AI Classics

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.


18 Validation of a Weather Forecasting Expert System S. Zubrick

AI Classics

A thunderstorm is considered severe if any one of the following phenomena accompanies the thunderstorm (and is reported): - tornadoes (intense, small-scale cyclones); - hailstones cm (in.) in diameter; - surface wind gusts in excess of 93 km h-1 (50 knots) and/or significant wind damage.


Z.til

AI Classics

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.




Report 81 09 Evaluating Expert Systems . Stanford Edward H. Jul 1981

AI Classics

This paper is the author's contribution to Chapter 6. in the volume BU:LDIN3 EXPERT SYSTEMS, edited by R. Hayes-Roth, D. Lenat, and D. Waterman:4 The full article is entitled "Evaluation of expert systems: issues and case studies", and is authored by J. Gaschnig, P. Klahr, H. Pople, E. Shortliffe. The volume is the result of a Workshop on Expert Systems held in San Diego in August 1980 and sponsored by the Rand Corporation, ARPA, and the NSF. Parts of Chapters 7 & 8. Reprinted with permission. In this section we define ma,ly of the parameters that determine an appropriate design for an evaluation experiment. When one examines the literature on computer performance evaluation, it is clear that the term is used with a variety of meanings depending upon the;.-c.3pective of tne authors. Each tends to focus on the specific performance issues that have been most central to the design of the system in question. Utner aspects warranting formal evaluation are often ignored.