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)     

A hybrid PC/PLC architecture for manufacturing-system control—theory and implementation

This paper presents a novel and generic PC/PLC-based software/hardware architecture for the control of flexible manufacturing workcells. The proposed implementation methodology is based on the utilization of any one of the available formal discrete-event-system control theories in conjunction with state-of-the-art industrial programmable-logic controllers (PLCs). The methodology has been illustrated to be a viable technique through its actual implementation in our laboratory using a robotic-workcell testbed. The specific control theory used is a combination of Extended Moore Automata and Ramadge-Wonham Automata that has been developed by our research group. The modular control software architecture has been developed for MS-Windows environments (running on one PC interfaced to the PLCs) and allows the use of different formal control theories as well as different commercial PLC hardware. The effective graphical user interface provides a transparent programming environment, where users are not expected to have a full knowledge of the formal control theory used.     

Quantitative estimation: From the real world to the psychology lab.

Banaji and Crowder (see record 1990-00387-001) contend "that the movement to develop an ecologically valid psychology of memory has proven itself largely bankrupt" (p. 1185). The authors have two primary concerns. First, they believe that it is rare for studies of real-world cognition to support conclusions that generalize broadly. Second, they are concerned that such studies may be inherently incapable of making significant theoretical or methodological contributions. The research program outlined here can be viewed as a response (in the form of a case study) to the Banaji and Crowder position. Specifically, this project has adopted both theoretical and methodology perspectives from the study of real-world memory to address a long-standing issue in the mainstream memory literature. The project itself was designed to characterize the strategies used to estimate event frequency and the conditions that foster the use of these strategies. A valid model of the estimation process is necessary in order to understand the encoding and representation of event frequency. (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.