R1-soar: an experiment in knowledge-intensive programming in a problem-solving architecture

This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.     

UVB irradiation alters cellular responses to cytokines: role in extracellular matrix gene expression

Chronic myeloid leukemia course was evaluated versus sex in 271 patients. Chronic stage involved more pronounced leukocytosis, thrombocytosis and splenomegaly in females, the latter showing higher susceptibility to anemia. As a result, treatment has to deal with a greater mass of tumor. A relatively longer survival time in males (44 and 42 months, respectively) suggest a higher effectiveness of therapy in such patients. When diagnosed, leukocytosis, thrombocytosis, enhanced splenomegaly and anemia should be regarded as factors of unfavorable prognosis.     

Chunking in Soar: The Anatomy of a General Learning Mechanism

In this article we describe an approach to the construction of a general learning mechanism based on chunking in Soar. Chunking is a learning mechanism that acquires rules from goal-based experience. Soar is a general problem-solving architecture with a rule-based memory. In previous work we have demonstrated how the combination of chunking and Soar could acquire search-control knowledge (strategy acquisition) and operator implementation rules in both search-based puzzle tasks and knowledge-based expert-systems tasks. In this work we examine the anatomy of chunking in Soar and provide a new demonstration of its learning capabilities involving the acquisition and use of macro-operators.     

Effectiveness evaluation of simulative workshops for newly licensed drivers

The current study set to examine the effects of simulator use in driving instruction on newly licensed drivers, comparing the road safety knowledge and reported intended behavior, as well as the actual driving performance of new drivers. Participants consisted of 280 newly licensed driver, of which 140 whose drivers license training included additional simulator-based lessons, and 140 drivers whose training precluded simulator-based lessons. All drivers answered questionnaires pertaining to their intended safe driving behaviors (according to Ajzen's (2000) theory of planned behavior), and to their traffic safety knowledge. Of the initial sample, 40 drivers received actual driving performance evaluation by an expert driving instructor, as well as by in-vehicle data recorders (IVDRs). We assumed that safer drivers report safer driving intentions, demonstrate greater traffic safety knowledge, evaluated as safer drivers by the driving instructor, and display lower and stable driving parameters on the IVDRs. We hypothesized that theoretical driving studies combined with practical training on simulators will elevate the safety level of novices driving. Hierarchical regression analyses on driving intentions indicated that drivers who did not receive simulator-based lessons demonstrated safer driving intentions compared to drivers who received simulator-based lessons. This pattern possibly indicating the drivers who received simulator-based lessons felt more confident in their driving abilities compared to drivers who did not receive simulated training. No significant difference was found in traffic safety knowledge, or in the evaluation of the expert driving instructor. IDVR data comparisons indicated drivers who received simulator-based lessons braked more often and were less prone to headway events, suggesting a more responsive driving style. These findings do not point to any significant advantage or disadvantage of the current simulator-based driving training over other driving training methods.     

Traffic light compliance by civilians, soldiers and military officers

Military officers, particularly those in the chain of command, are expected to provide “a good example of virtue, honor, patriotism, and subordination” (Snider, 2008). The aim of the present study was to examine the extent to which these values are reflected in the behavior that military officers exhibit crossing roads, as compared with soldiers’ and civilians’ road-crossing behavior. One thousand two hundred pedestrians were observed while crossing the street at an urban intersection in the center of Israel. Of these pedestrians, 594 (49.5%) were soldiers, 112 (9.33%) were officers and 488 (40.66%) were civilians. An observation grid was constructed to register pedestrians’ crossing behavior. The independent variables encoded were gender, military status and military rank. The dependent variables encoded were crossing the road at a red light; crossing a busy road; running across the road; crossing the road diagonally; crossing the road without looking both ways; and crossing without first stopping at the sidewalk edge. To compare the crossing behaviors of each group of pedestrians, a summation was made for the six parameters of safe crossing. An ANOVA compared the means of unsafe road-crossing behaviors of males and females, on the one hand, and of civilians, soldiers and officers, on the other. A post hoc Scheffe test conducted on the means showed that the mean of the unsafe road-crossing behaviors of the civilians (M = 1.55, SE = .04) was higher than that of the soldiers (M = 1.35, SE = .04) and of the officers (M = 1.21, SE = .08) p < .05. No significant difference was found between the means of the unsafe road-crossing behaviors of soldiers and officers, although the means of the officers’ unsafe behaviors was lower than that of the soldiers. That is, both soldiers and officers exhibited road-crossing behavior that was significantly safer than that of civilians. Generally, more females waited for the green light (54.1%) than males (45.9%). No main effect of gender or interaction with belonging to the military was found.