Terry Rankin

Cognitive Inquiry and the Philosophy of Mind.- Prologue: What is Mind?.- Current Issues in the Philosophy of Mind.- I: Computational Conceptions.- Machines and the Mental.- What's in a Mind?.- II: Connectionist Conceptions.- Connectionism, Eliminativism, and the Future of Folk Psychology.- On the Proper Treatment of Connectionism.- III: Representational Conceptions.- Semantics, Wisconsin Style.- Cognitive Science and the Problem of Semantic.- IV: Mentality and Intentionally.- The Primacy of the Intention.- Intentionality and Its Place in Nature.- V: Epistemology and Cognition.- Why Reason Can't Be Naturalized.- The Relation Between Epistemology and Psychology.- VI: The Mental and the Physical.- Two Versions of the Identity Theory.- A Bridge Between Cognitive Science and Neuroscience: The Functional Architecture of Mind.- Epilogue: Conflicting Conceptions.- Language and Mentality: Computational, Representational, and Dispositional Conceptions.- Selected Bibliography.- Index o...

A Nonmonotonic Inference Engine People are often forced by circumstances to make judgments based on incomplete information. These circumstances do not disappear when we augment our native reasoning ability with the use of knowledge bases and automated reasoning systems. It is therefore extremely important that our systems be able to assist us in this kind of reasoning. Frequently, the best conclusion that can be drawn from an incomplete set of facts about a situation are different from the best conclusion that can be drawn from a complete or nearly complete superset of the same facts. The set of conclusions we draw as our information increases does not simply change in one direction or monotonically by getting larger; it can also shrink as our previous best conclusions are rejected on the basis of new information. ability to evaluate conditional goals. The natural method for doing this results in a treatment of the monotonic rules of PROLOG which is much closer to intuitionist logic than to classical logic. Evaluation of strong nonmonotonic rules or subjunctive conditionals as goals is also implemented in N-PROWIS. With the evaluation of conditional goals, it becomes possible to incorporate conditionals into the bodies of rules in the system.

‘Computer’ and ‘AI artifact’ are largely synonymous, since they constitute roughly the same ref-erence class and identify the same attribute properties – especially since the primary (or even the only) difference between them is that the AI artifact is explicitly conceived and designed to imi-tate human intelligence under the Turing Test Paradigm, whereas this emulation is not a funda-mental consideration in the more general architecture and production of computers. But AI sci-ence need not adopt that test and corresponding paradigm as being indispensable for a genuinely scientific program of applied epistemology. The discussion that follows seeks to open this ques-tion further, at least, and reveal a problem that appears to be overlooked on most Turing ap-proaches to AI science: those criteria of artifact intelligence invite a paradox, and they might not successfully identify the kind of mentality, human or not, that should be optimized in the design of a useful artifact, after all. 

[1989 publication with current “Postscript” update added February 2018.]