This paper investigates the appropriateness of formal dialectics as a basis for non-monotonic reasoning and defeasible reasoning that takes computational limits seriously. Rules that can come into conflict should be regarded as policies, which are inputs to deliberative processes. Dialectical protocols are appropriate for such deliberations when resources are bounded and search is serial. AI, it is claimed here, is now perfectly positioned to correct many misconceptions about reasoning that have resulted from mathematical logic's enormous success in this century: among them, (1) that all reasons are demonstrative, (2) that rational belief is constrained, not constructed, (3) that process and disputation are not essential to reasoning. AI mainly provides new impetus to formalize the alternative (but older) conception of reasoning, and AI provides mechanisms with which to create compelling formalism that describes the control of processes. The technical contributions here are: the partial justification of dialectic based on controlling search; the observation that non-monotonic reasoning can be subsumed under certain kinds of dialectics; the portrayal of inference in knowledge bases as policy reasoning; the review of logics of dialogue and proposed extensions; and the pre-formal and initial formal discussion of aspects and variations of dialectical systems with non-demonstrative reasons. 1. ARGUMENTS AND DEMONSTRATION

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. In this paper we argue for a Separability Hypothesis as a working hypothesis for AI. There is no a priori equivalence of the terms "mind" and "cognition." Phenomenologically, a mental state has a cognitive content as well as subjectivity and emotions. The Separability Hypothesis asserts that an architecture for intelligence is a homomorphism of the architecture for mind. None of the technical problems that AI is working on today require machines which have anything other than a cognitive content to them. We discuss how this Hypothesis protects AI from a number of its critics, and lets the field get on with its technical concerns. Is Intelligence Separable from other Mental Phenomena? In both cognitive science and AI, the terms "mind" and "cognition" (or "intelligence") are generally presumed to refer to one and the same thing. This is not an explicit doctrine of the fields in question, but more of a tacit assumption. Thus, both people within and outside AI take the questions, "Can ...

In this paper we argue for a Separability Hypothesis as a working hypothesis for AI. There is no a priori equivalence of the terms "mind" and "cognition." Phenomenologically, a mental state has a cognitive content as well as subjectivity and emotions. The Separability Hypothesis asserts that an architecture for intelligence is a homomorphism of the architecture for mind. None of the technical problems that AI is working on today require machines which have anything other than a cognitive content to them. We discuss how this Hypothesis protects AI from a number of its critics, and lets the field get on with its technical concerns.