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A Theory of Heuristic Reasoning About Uncertainty

AI Magazine

This article describes a theory of reasoning about uncertainly, based on a representation of states of certainly called endorsements. The theory of endorsements is an alternative to numerical methods for reasoning about uncertainly, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda hart, and Nilsson, 1976) and Shafer-dempster theory (Shafer, 1976). The fundamental concern with numerical representations of certainty is that they hide the reasoning about uncertainty. While numbers are easy to propagate over inferences, what the numbers mean is unclear. The theory of endorsements provide a richer representation of the factors that affect certainty and supports multiple strategies for dealing with uncertainty.


Artificial Intelligence: Some Legal Approaches and Implications

AI Magazine

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups. This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition. As an alternative analysis, this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend.



Towards a Taxonomy of Problem Solving Types

AI Magazine

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.


Research at Fairchild

AI Magazine

The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.


Introduction to the COMTEX Microfiche Edition of the Early MIT Artificial Intelligence Memos

AI Magazine

These are the voyages of the MIT Artificial Intelligence Laboratory, and these remarks may help to understand the context of this collection, though in many ways the memoranda speak quite clearly for themselves and my comments are not, in any case, to be regarded as history, for I have written them quite hastily, in much the same spirit of the memos themselves, when it was our strategy in those early days to be unscholarly: we tended to assume, for better or for worse, that everything we did was so likely to be new that there was little need for caution or for reviewing literature or for double -checking anything. As luck would have it, that almost always turned out true.


Psychological Studies and Artificial Intelligence

AI Magazine

This paper argues for the position that experimental human studies are relevant to most facets of AI research and that closer ties between AI and experimental psychology will enhance the development of booth the principles of artificial intelligence and their implementation in computers. Raising psychological assumptions from the level of ad hoc intuitions to the level of systematic empirical observation, in the long run, will improve the quality of AI research and help to integrate it with related studies in other disciplines.



Introduction to the COMTEX Microfiche Edition of the Early MIT Artificial Intelligence Memos

AI Magazine

These are the voyages of the MIT Artificial Intelligence Laboratory, and these remarks may help to understand the context of this collection, though in many ways the memoranda speak quite clearly for themselves and my comments are not, in any case, to be regarded as history, for I have written them quite hastily, in much the same spirit of the memos themselves, when it was our strategy in those early days to be unscholarly: we tended to assume, for better or for worse, that everything we did was so likely to be new that there was little need for caution or for reviewing literature or for double -checking anything. As luck would have it, that almost always turned out true.


On the Discovery and Generation of Certain Heuristics

AI Magazine

This paper explores the paradigm that heuristics are discovered by consulting simplified models of the problem domain. After describing the features of typical heuristics on some popular problems, we demonstrate that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues. We then outline a scheme for generating such heuristics mechanically, which involves systematic refinement and deletion of constraints from the original problem specification until a semidecomposable model is identified. The solution to the latter constitutes a heuristic for the former.