The Examples of Human Commonsense Reasoning Processes

2010 ◽  
pp. 102-121
Author(s):  
Xenia Naidenova
Keyword(s):  
Pattern Recognition ◽  
Human Reasoning ◽  
Commonsense Reasoning ◽  
Scientific Investigations

In this chapter, we concentrate our attention on analyzing and modeling natural human reasoning in solving different tasks: pattern recognition in scientific investigations, logical games, and investigation of crimes.

The Logical Rules of Commonsense Reasoning

2010 ◽  
pp. 88-101
Author(s):  
Xenia Naidenova
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Ore Deposits ◽  
Spectral Information ◽  
Human Reasoning ◽  
Commonsense Reasoning ◽  
Inference Mechanism ◽  
Testing Data ◽  
Information Image ◽  
Logical Rules

In this chapter we describe a model of commonsense reasoning that has been acquired from our numerous investigations on the human reasoning modes used by experts for solving diagnostic problems in diverse areas such as pattern recognition of natural objects (rocks, ore deposits, types of trees, types of clouds etc.), analysis of multi-spectral information, image processing, interpretation of psychological testing data, medicine diagnosis and so on. The principal aspects of this model coincide with the rulebased inference mechanism that is embodied in the KADS system (Ericson, et al., 1992), (Gappa, & Poeck, 1992). More details related to our model of reasoning and its implementation can be found in (Naidenova, & Syrbu, 1984; Naidenova, & Polegaeva, 1985a; 1985b).

A little too technical: The threat of intellectualising technical reasoning

2020 ◽  
Vol 43 ◽  
Author(s):  
Ian Robertson
Keyword(s):  
Social Cognition ◽  
Human Reasoning ◽  
Technological Culture ◽  
Technical Reasoning

Abstract Osiurak and Reynaud (O&R) claim that research into the origin of cumulative technological culture has been too focused on social cognition and has consequently neglected the importance of uniquely human reasoning capacities. This commentary raises two interrelated theoretical concerns about O&R's notion of technical-reasoning capacities, and suggests how these concerns might be met.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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