Defeasible Decisions: What the Proposal Is and Isn\u27t

In two recent papers, I have proposed a description of decision analysis that differs from the Bayesian picture painted by Savage, Jeffrey and other classic authors. Response to this view have been either overly enthusiastic or unduly pessimistic. In this paper I try to place the idea in its proper place, which must be somewhere in between. Looking at decision analysis as defeasible reasoning produces a framework in which planning and decision theory can be integrated, but work on the details has barely begun. It also produces a framework in which the meta-decision regress can be stopped in a reasonable way, but it does not allow us to ignore meta-level decisions. The heuristics for producing arguments that I have presented are only supposed to be suggestive; but they are not open to the egregious errors about which some have worried. And though the idea is familiar to those who have studied heuristic search, it is somewhat richer because the control of dialectic is more interesting than the deepening of the search

Ampliative Inference, Computation, and Dialectic

There are three theses here: • Non-computationally conceived inference merely expands notation. This includes induction as well as deduction, and thus both deserve the adjective non-ampliative. Deriving entailments merely expands shorthand. All of the familiar formalisms for reasoning do just this. • There now exist examples of formalism for reasoning that do something else. They are deliberative, and to say in what way they are deliberative requires reference to the process through which they compute their entailments. • The original ampliative/non-ampliative terminology best survives as referring to this new distinction. Viewed formally, all other attempted distinctions either presume deduction to be privileged, or else fail to separate inference that actually tells us something new from inference that simple rehashes what has already been represented

Defeat Among Arguments II

This technical report consists of three chapters from a larger manuscript that was a finalist in the 1988 Journal of Philosophy Johnsonian competition. These three chapters together represent a revised version of a paper that has been circulating under the title Defeat Among Arguments II since January 1988. Defeat Among Arguments updates my Computational Intelligence paper of 1987, which represented a novel way of formalizing defeasible reasoning, based on resolving competing arguments. The Yale Shooting Problem updates my Cognitive Science paper of 1987, and attempts a rebuttal of Hanks and McDermott\u27s evaluation in their 1987 Artificial Intelligence paper. The last section, Conventionalism and Non-Monotonicity, is a brief consideration of Touretzky, Thomason, and Horty\u27s Clash of Intuitions, and the prospects for choosing among languages for representing defeasible knowledge

Two Heuristic Functions for Decision

This paper investigates a different foundation for decision theory in which successive model refinement is central. The idea is to modify utility so that it can sometimes be calculated for an outcome without considering all the relevant properties that can be proved of the outcome, and without considering the utilities of its children. We build partially ordered heuristic utility functions. We treat the analysis of personal decision trees like heuristic search of game trees (taking expectations instead of doing minimax). Analysis of decision then becomes a process of constructing and evaluating defeasible arguments for decision. This leads to an iteratively improving computation of decision, or what Dean and Boddy have dubbed an anytime algorithm for decision. An axiomatization of this idea is simple in an existing system of defeasible reasoning. As a special case of defeasible reasoning, computing defeat among decision trees is also simple. The axioms for preference that lead to metric utility can be retained if we take the defeasibility to be a result of the epistemic problem of individuating objects of value. We say nothing yet about the specification of actual search strategies for particular forms of heuristic utility functions, though it is clearly a matter for further research

User\u27s Manual for CCRC: (Common Lisp Version) Computing Reference Classes Statistical Reasoning Shell v. 2.5

CCRC implements a subset of Kyburg\u27s rules for statistical inference. The system states from 1961 and is briefly described in The Reference Class, (H. Kyburg Philosophy of Science 50, 1982). Consult the paper Computing Reference Classes (R. Loui, in Kanal, L. and Lemmer, J., Uncertainty in AI, v.1, North-Holland 1987) for a precis of the ideas underlying this program. This document is only the skeleton of a manual. It is designed to get the novice on the program as quickly as possible, and to provide some guidance for advanced questions. This piece of software is the extended version of a prototype principally intended to assist AI research on reasoning with uncertainty. This program is a small prototype extended so that it can be patched into larger experimental systems

Analogical Reasoning, Defeasible Reasoning, and the Reference Class

This paper attempts four things. It demonstrates the possibility of accounting for Russell-style and Clark-style analogical reasoning in an existing framework for statistical reasoning. It critically reviews the proposals made by Clark for defeasible analogical reasoning and shows how they can be understood better simply as defeasible reasoning. It argues that generalization from the single case is not as desirable as projection from the single case; the difference has to do with the defeasibility control strategy for statistical reasoning limited to a small number of cases

A Mathematical Treatment of Defeasible Reasoning and its Implementation

We present a mathematical approach to defeasible reasoning. This approach is based on the notion of specificity introduced by Poole and the theory of warrant presented by Pollock. We combine the ideas of the two. This main contribution of this paper is a precise well-defined system which exhibits correct behavior when applied to the benchmark examples in the literature. We prove that an order relation can be introduced among equivalence classes under the equi-specificity relation. We also prove a theorem that ensures the termination of the process of finding the justified facts. Two more lemmas define a reduced search space for checking specificity. In order to implement the theoretical ideas, the language is restricted to Horn clauses for the evidential context. The language used to represent defeasible rules has been restricted in a similar way. The authors intend this work to unify the various existing approaches to argument-based defeasible reasoning