IsaLog¬: a deductive language with negation for complex-object databases with hierarchies

1997 ◽  
Vol 24 (1) ◽  
pp. 1-38 ◽  
Author(s):  
Paolo Atzeni ◽  
Luca Cabibbo ◽  
Giansalvatore Mecca
Keyword(s):  
Complex Object ◽  
Object Databases

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

DECOMPOSITION METHOD FOR DETERMINING THE HIGH REFLECTED SECTIONS OF A COMPLEX OBJECT SURFACE

2018 ◽  
Vol 77 (11) ◽  
pp. 945-956 ◽  
Author(s):  
N. N. Kolchigin ◽  
M. N. Legenkiy ◽  
A. A. Maslovskiy ◽  
А. Demchenko ◽  
S. Vinnichenko ◽  
...  
Keyword(s):  
Decomposition Method ◽  
Complex Object ◽  
Object Surface

Hierarchical composition in complex object drawing

2019 ◽  
Author(s):  
Michiru Makuuchi
Keyword(s):  
Hierarchical Structure ◽  
Homo Sapiens ◽  
Hierarchical Structures ◽  
Target Object ◽  
Point Of View ◽  
Complex Object ◽  
Complex Objects ◽  
Cognitive Domains ◽  
Adult Participants ◽  
Symbolic Cognition

Symbolic behaviours such as language, music, drawing, dance, etc. are unique to humans and are found universally in every culture on earth1. These behaviours operate in different cognitive domains, but they are commonly characterised as linear sequences of symbols2,3. One of the most prominent features of language is hierarchical structure4, which is also found in music5,6 and mathematics7. Current research attempts to address whether hierarchical structure exists in drawing. When we draw complex objects, such as a face, we draw part by part in a hierarchical manner guided by visual semantic knowledge8. More specifically, we predicted how hierarchical structure emerges in drawing as follows. Although the drawing order of the constituent parts composing the target object is different amongst individuals, some parts will be drawn in succession consistently, thereby forming chunks. These chunks of parts would then be further integrated with other chunks into superordinate chunks, while showing differential affinity amongst chunks. The integration of chunks to an even higher chunk level repeats until finally reaching the full object. We analysed the order of drawing strokes of twenty-two complex objects by twenty-five young healthy adult participants with a cluster analysis9 and demonstrated reasonable hierarchical structures. The results suggest that drawing involves a linear production of symbols with a hierarchical structure. From an evolutionary point of view, we argue that ancient engravings and paintings manifest Homo sapiens’ capability for hierarchical symbolic cognition.

scholarly journals Evaluating the Reaction to a Complex Rotated Object in the American Quarter Horse (Equus caballus)

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1383
Author(s):  
Megan Elizabeth Corgan ◽  
Temple Grandin ◽  
Sarah Matlock
Keyword(s):  
Behavioral Responses ◽  
Equus Caballus ◽  
T Test ◽  
Complex Object ◽  
The Novel ◽  
Novel Object ◽  
Quarter Horse ◽  
First Pass ◽  
Young Horses ◽  
Familiar Objects

It is dangerous for both riders and horses when a horse suddenly startles. Sometimes horses do this in familiar environments because familiar objects may look different when rotated. The purpose of this study was to determine whether horses that had been habituated to a complex object (children’s playset) would react to the object as novel when rotated 90 degrees. Twenty young horses were led past the playset 15 times by a handler. Next, the rotated group was led past the rotated playset 15 times. Each time the horse was led by the object was a pass. The behavioral responses observed and analyzed were ears focused on the object, nostril flares, neck raising, snort, avoid by stopping, avoid by moving feet sideways, and avoid by flight. An increasing reactivity scale was used to quantify behavioral responses. A two-sample t-test was performed on the reactivity scores comparing the first pass by the novel object to the first pass by the rotated object. The horses in the rotated group reacted to the rotated orientation similarly to the first exposure (p = 0.001, α < 0.05). Being aware of potential reactions to changes in previously familiar environments can help keep the handler safer.

scholarly journals Using fMR-adaptation to track complex object representations in perirhinal cortex

2013 ◽  
Vol 4 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Rachael D. Rubin ◽  
Samantha A. Chesney ◽  
Neal J. Cohen ◽  
Brian D. Gonsalves
Keyword(s):  
Perirhinal Cortex ◽  
Complex Object ◽  
Object Representations
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