scholarly journals Animating Cognitive Models and Architectures: A Rule-Based Approach

10.29007/wjwz ◽  
2018 ◽  
Author(s):  
Nada Sharaf ◽  
Slim Abdennadher ◽  
Thom Fruehwirth ◽  
Daniel Gall

Computational psychology provides computational models exploring different aspects of cognition. A cognitive architecture includes the basic aspects of any cognitive agent. It consists of different correlated modules. In general, cognitive architectures provide the needed layouts for building intelligent agents. The paper presents the a rule-based approach to visually animate the simulations of models done through cognitive architectures. As a proof of concept, simulations through Adaptive Control of Thought-Rational (ACT-R) were animated. ACT-R is a well-known cognitive architecture. It was deployed to create models in different fields including, among others, learning, problem solving and languages.

2019 ◽  
Author(s):  
Matthew C. Pharris ◽  
Thomas M. Bartol ◽  
Terrence J. Sejnowski ◽  
Mary B. Kennedy ◽  
Melanie I. Stefan ◽  
...  

AbstractCa2+/calmodulin-dependent protein kinase II (CaMKII) accounts for up to 2 percent of all brain protein and is essential to memory function. CaMKII activity is known to regulate dynamic shifts in the size and signaling strength of neuronal connections, a process known as synaptic plasticity. Increasingly, computational models are used to explore synaptic plasticity and the mechanisms regulating CaMKII activity. Conventional modeling approaches may exclude biophysical detail due to the impractical number of state combinations that arise when explicitly monitoring the conformational changes, ligand binding, and phosphorylation events that occur on each of the CaMKII holoenzyme’s twelve subunits. To manage the combinatorial explosion without necessitating bias or loss in biological accuracy, we use a specialized syntax in the software MCell to create a rule-based model of the twelve-subunit CaMKII holoenzyme. Here we validate the rule-based model against previous measures of CaMKII activity and investigate molecular mechanisms of CaMKII regulation. Specifically, we explore how Ca2+/CaM-binding may both stabilize CaMKII subunit activation and regulate maintenance of CaMKII autophosphorylation. Noting that Ca2+/CaM and protein phosphatases bind CaMKII at nearby or overlapping sites, we compare model scenarios in which Ca2+/CaM and protein phosphatase do or do not structurally exclude each other’s binding to CaMKII. Our results suggest a functional mechanism for the so-called “CaM trapping” phenomenon, such that Ca2+/CaM structurally excludes phosphatase binding and thereby prolongs CaMKII autophosphorylation. We conclude that structural protection of autophosphorylated CaMKII by Ca2+/CaM may be an important mechanism for regulation of synaptic plasticity.Author summaryIn the hippocampus, the dynamic fluctuation in size and strength of neuronal connections is thought to underlie learning and memory processes. These fluctuations, called synaptic plasticity, are in-part regulated by the protein calcium/calmodulin-dependent kinase II (CaMKII). During synaptic plasticity, CaMKII becomes activated in the presence of calcium ions (Ca2+) and calmodulin (CaM), allowing it to interact enzymatically with downstream binding partners. Interestingly, activated CaMKII can phosphorylate itself, resulting in state changes that allow CaMKII to be functionally active independent of Ca2+/CaM. Phosphorylation of CaMKII at Thr-286/287 has been shown to be a critical component of learning and memory. To explore the molecular mechanisms that regulate the activity of CaMKII holoenzymes, we use a rule-based approach that reduces computational complexity normally associated with representing the wide variety of functional states that a CaMKII holoenzyme can adopt. Using this approach we observe regulatory mechanisms that might be obscured by reductive approaches. Our results newly suggest that CaMKII phosphorylation at Thr-286/287 is stabilized by a mechanism in which CaM structurally excludes phosphatase binding at that site.


1999 ◽  
Vol 28 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Peter Desain ◽  
Henkjan Honing

Author(s):  
Enrique Osuna ◽  
Sergio Castellanos ◽  
Jonathan Hernando Rosales ◽  
Luis-Felipe Rodríguez

Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs


2013 ◽  
Vol 5 (1) ◽  
pp. 79-89 ◽  
Author(s):  
Igor Wojnicki

One of the main reasons of using a rule-based approach to program control systems is that they can be formally verified. For such systems communication with the environment is often encoded within the knowledge base. Such inclusion may lead to issues with portability, extendibility, maintainability, and interoperability. The paper proposes a four layer architecture to solve these issues. A proof-of-concept RBS, targeted at control systems, and an example case are also given.


Author(s):  
Padmavathi .S ◽  
M. Chidambaram

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.


2019 ◽  
Vol 50 (2) ◽  
pp. 98-112 ◽  
Author(s):  
KALYAN KUMAR JENA ◽  
SASMITA MISHRA ◽  
SAROJANANDA MISHRA ◽  
SOURAV KUMAR BHOI ◽  
SOUMYA RANJAN NAYAK

2010 ◽  
Vol 12 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Xueying ZHNAG ◽  
Guonian LV ◽  
Boqiu LI ◽  
Wenjun CHEN

Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.


Author(s):  
Supriya Raheja ◽  
Geetika Munjal ◽  
Jyoti Jangra ◽  
Rakesh Garg

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