Intellectual performance and ego depletion: Role of the self in logical reasoning and other information processing.

Some complex thinking requires active guidance by the self, but simpler mental activities do not. Depletion of the self's regulatory resources should therefore impair the former and not the latter. Resource depletion was manipulated by having some participants initially regulate attention (Studies 1 and 3) or emotion (Study 2). As compared with no-regulation participants who did not perform such exercises, depleted participants performed worse at logic and reasoning (Study 1), cognitive extrapolation (Study 2), and a test of thoughtful reading comprehension (Study 3). The same manipulations failed to cause decrements on a test of general knowledge (Study 2) or on memorization and recall of nonsense syllables (Study 3). Successful performance at complex thinking may therefore rely on limited regulatory resources. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Conditions for the Acceptance of Deontic Conditionals.

Recent psychological research has investigated how people assess the probability of an indicative conditional. Most people give the conditional probability of q given p as the probability of if p then q. Asking about the probability of an indicative conditional, one is in effect asking about its acceptability. But on what basis are deontic conditionals judged to be acceptable or unacceptable? Using a decision theoretic analysis, we argue that a deontic conditional, of the form if p then must q or if p then may q, will be judged acceptable to the extent that the p & q possibility is preferred to the p & not-q possibility. Two experiments are reported in which this prediction was upheld. There was also evidence that the pragmatic suitability of permission rules is partly determined by evaluations of the not-p & q possibility. Implications of these results for theories of deontic reasoning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Synthesizing plans that contain actions with context-dependent effects1

This paper presents a method of solving planning problems that involve actions whose effects change according to the situations in which they are performed. The approach is an extension of the conventional planning methodology in which plans are constructed through an iterative process of scanning for goals that are not yet satisfied, inserting actions to achieve them, and introducing subgoals to these actions. This methodology was originally developed under the assumption that one would be dealing exclusively with actions that produce the same effects in every situation. The extension involves introducing additional subgoals to actions above and beyond the preconditions of execution normally introduced. These additional subgoals, called secondary preconditions, ensure that the actions are performed in contexts conducive to producing the effects we desire. This paper defines and analyzes secondary preconditions from a mathematically rigorous standpoint and demonstrates how they can be derived from regression operators.     

Casual reasoning in planning1

Reasoning about actions necessarily involves tracking the truth of assertions about the world over time. The SIPE planning system retains the efficiency of the STRIPS assumption for this while enhancing expressive power by allowing the specification of a causl theory. Separation of knowledge about causality from knowledge about actions relieves operators of much of their representational burden and allows them to be applicable in a wide range of contexts. The implementation of causal theories is described, together with examples and evaluations of the system's expressive power and efficiency.     

Application of parallelized analogical planning to engineering design

Analogical planning provides a means of solving engineering problems where other machine learning methods fail. Unlike many machine learning paradigms, analogy does not require numerous previous examples or a rich domain theory. Instead, analogical planners adapt knowledge of solved problems in similar domains to the current problem. Unfortunately, the analogical planning task is an expensive one. While the process of forming correspondences between a known problem and a new problem is complex, the problem of selecting a base case for the analogy is virtually intractable.This paper addresses the issue of efficiently forming analogical plans. The Anagram planning system is described, which takes advantage of the massively parallel architecture of the Connection Machine to perform base selection and map formation. Anagram provides a tractable solution to analogical planning, with a complexity that is sublinear in the size of the plans.This paper describes the Anagram system and its parallel algorithms. The paper also presents theoretical analyses and empirical results of testing the system on a large database of plans from the domain of automatic programming.     

Notes on Ginsberg's multivalued logics

The use of multivalued logics for knowledge representation and nonmonotonic reasoning has often been advocated, in particular within the general framework proposed by Ginsberg in his paper "Multivalued logics: a uniform approach to reasoning in artificial intelligence." His system is based on a multivalued logic with an arbitrary number of truth values classified with respect to two partial orders, a truth order and a knowledge order. This classification is very interesting and gives an intuitive appeal to the framework. In this paper the work by Ginsberg is critically reviewed, pointing out some flaws and ways to overcome them. Moreover, we present some ideas on how to modify the original schema in order to obtain a more semantically well-founded framework.
L'utilisation de la Iogique multivalente pour la représentation des connaissances et le raisonnement non monotone a souvent été préconisée, en particulier à l'intérieur du cadre général proposé par Ginsberg dans son article intitulé〘 Multivalued logics: a uniform approach to reasoning in artificial intelligence 〙 Son système est basé sur une logique multivalente comportant un nombre arbitraire de valeurs de vérité classées selon deux ordres partiels: un ordre de vérité et un ordre de connaissances. Cette classification est très intéressante et donne un attrait intuitif au cadre. Dans cet article, l'auteur examine le travail de Ginsberg, y relève des lacunes et propose des moyens de les corriger. De plus, il expose certaines idées en vue de modifier le schéma original et ainsi obtenir un meilleur cadre du point de vue de la sémantique.     

Integrating integer programming and probabilistic deduction graphs for probabilistic reasoning

Optimal solutions of several variants of the probabilistic reasoning problem were found by a new technique that integrates integer programming and probabilistic deduction graphs (PDG). PDGs are extended from deduction graphs of the and-type via normal deduction graphs. The foregoing variants to be solved can involve multiple hypotheses and multiple evidences where the former is given and the latter is unknown and being found or vice versa. The relationship among these hypotheses and evidences with possible intermediaries is represented by a causal graph. The proposed method can handle a large causal graph of any type and find an optimal solution by invoking a linear integer programming package. In addition, formulating the reasoning problem to fit integer programming takes a polynomial time. H.-L. Li was visiting the Department of Computer Sciences, University of North Texas in 1988–1989. He is with the Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.