scholarly journals An Interoperable Framework for Computational Models of Emotion

2022 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Enrique Osuna ◽  
Sergio Castellanos ◽  
Jonathan Hernando Rosales ◽  
Luis-Felipe Rodríguez
Keyword(s):  
Intelligent Agents ◽  
Computational Models ◽  
Ad Hoc ◽  
Communication Process ◽  
Software Systems ◽  
Cognitive Agent ◽  
Agent Architectures ◽  
Cognitive Components ◽  
Cognitive Information ◽  
Computational Models Of Emotion

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

Modulation Scheme for Biasing the Emotional Process of Autonomous Agents

2020 ◽  
pp. 339-361
Author(s):  
Sergio Castellanos ◽  
Luis-Felipe Rodríguez ◽  
J. Octavio Gutierrez-Garcia
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Cognitive Architecture ◽  
Modulation Scheme ◽  
Membership Functions ◽  
Real World Data ◽  
Emotional Process ◽  
Fuzzy Membership Functions ◽  
Cognitive Components ◽  
Cognitive Information

Autonomous agents (AAs) are capable of evaluating their environment from an emotional perspective by implementing computational models of emotions (CMEs) in their architecture. A major challenge for CMEs is to integrate the cognitive information projected from the components included in the AA's architecture. In this chapter, a scheme for modulating emotional stimuli using appraisal dimensions is proposed. In particular, the proposed scheme models the influence of cognition on appraisal dimensions by modifying the limits of fuzzy membership functions associated with each dimension. The computational scheme is designed to facilitate, through input and output interfaces, the development of CMEs capable of interacting with cognitive components implemented in a given cognitive architecture of AAs. A proof of concept based on real-world data to provide empirical evidence that indicates that the proposed mechanism can properly modulate the emotional process is carried out.

A Flexible Scheme to Model the Cognitive Influence on Emotions in Autonomous Agents

2018 ◽  
Vol 12 (4) ◽  
pp. 81-100
Author(s):  
Sergio Castellanos ◽  
Luis-Felipe Rodríguez
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Computational Simulation ◽  
Cognitive Model ◽  
Human Information Processing ◽  
Appraisal Theory ◽  
Cognitive Mechanisms ◽  
Integrative Framework ◽  
Cognitive Components ◽  
Cognitive Information

Autonomous agents (AAs) are designed to embody the natural intelligence by incorporating cognitive mechanisms that are applied to evaluate stimuli from an emotional perspective. Computational models of emotions (CMEs) implement mechanisms of human information processing in order to provide AAs for a capability to assign emotional values to perceived stimuli and implement emotion-driven behaviors. However, a major challenge in the design of CMEs is how cognitive information is projected from the architecture of AAs. This article presents a cognitive model for CMEs based on appraisal theory aimed at modeling AAs' interactions between cognitive and affective processes. The proposed scheme explains the influence of AAs' cognition on emotions by fuzzy membership functions associated to appraisal dimensions. The computational simulation is designed in the context of an integrative framework to facilitate the development of CMEs, which are capable of interacting with cognitive components of AAs. This article presents a case study and experiment that demonstrate the functionality of the proposed models.

Intelligent agents: theory and practice

1995 ◽  
Vol 10 (2) ◽  
pp. 115-152 ◽  
Author(s):  
Michael Wooldridge ◽  
Nicholas R. Jennings
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Short Review ◽  
Theory And Practice ◽  
Agent Technology ◽  
Software Systems ◽  
Agent Architectures ◽  
Agent Theory ◽  
Potential Applications ◽  
Engineering Models

AbstractThe concept of anagenthas become important in both artificial intelligence (AT) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary).Agent theoryis concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents.Agent architecturescan be thought of as software engineering models of agents; researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally,agent languagesare software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper isnotintended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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
Keyword(s):  
Intelligent Agents ◽  
Computational Models ◽  
Cognitive Architecture ◽  
Proof Of Concept ◽  
Cognitive Agent ◽  
Cognitive Architectures ◽  
Learning Problem ◽  
Rule Based ◽  
Computational Psychology ◽  
Rule Based Approach

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.

scholarly journals Modelling Software Architecture for Visual Simultaneous Localization and Mapping

Automation ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 48-61
Author(s):  
Bhavyansh Mishra ◽  
Robert Griffin ◽  
Hakki Erhan Sevil
Keyword(s):  
System Integration ◽  
Ad Hoc ◽  
Simultaneous Localization And Mapping ◽  
Software Systems ◽  
Model Driven ◽  
Performance Improvements ◽  
Software Model ◽  
Localization And Mapping ◽  
Stereo Cameras ◽  
Core Components

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.

scholarly journals Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

Simulation on the Web with distributed models and intelligent agents

SIMULATION ◽  
2012 ◽  
Vol 88 (9) ◽  
pp. 1080-1092 ◽  
Author(s):  
András Jávor ◽  
Attila Fűr
Keyword(s):  
Intelligent Agents ◽  
Distributed Simulation ◽  
Simulation System ◽  
Simulation Software ◽  
Software Systems ◽  
Web Based ◽  
Distributed Models ◽  
Adaptive Simulation ◽  
Sample Application ◽  
Great Complexity

Simulation is aimed very often to solve problems of great complexity requiring – beyond using the advanced simulation software tools – platforms that enable the implementation of such software systems. In recent years the concept of cloud computing has emerged and is being applied more and more widely for solving such problems. This paper, beyond delineating the main trends of the development of distributed simulation over a grid, especially over the Internet through Web-based applications, highlights the concepts of service-based simulation system approach. This concept gives the possibility of implementing Web- or cloud agents and other ASP system compliant simulation services based on simulation standards. As a sample application, Fuzzy Web Service is demonstrated as a part of CASSANDRA 4.0 (Cognizant Adaptive Simulation System for Applications in Numerous Different Relevant Areas) that is developed by the McLeod Institute of Simulation Sciences Hungarian Center.

scholarly journals How to Validate in silico Deployment of Coronary Stents: Strategies and Limitations in the Choice of Comparator

2021 ◽  
Vol 3 ◽  
Author(s):  
Francesca Berti ◽  
Luca Antonini ◽  
Gianluca Poletti ◽  
Constantino Fiuza ◽  
Ted J. Vaughan ◽  
...  
Keyword(s):  
Numerical Model ◽  
Medical Devices ◽  
In Silico ◽  
Computational Models ◽  
Ad Hoc ◽  
Coronary Stent ◽  
Controlled Environment ◽  
Stent Deployment ◽  
Validation Plan ◽  
3D Printed

This study aims at proposing and discussing useful indications to all those who need to validate a numerical model of coronary stent deployment. The proof of the reliability of a numerical model is becoming of paramount importance in the era of in silico trials. Recently, the ASME V&V Standard Committee for medical devices prepared the V&V 40 standard document that provides a framework that guides users in establishing and assessing the relevance and adequacy of verification and validation activities performed for proving the credibility of models. To the knowledge of the authors, only a few examples of the application of the V&V 40 framework to medical devices are available in the literature, but none about stents. Specifically, in this study, the authors wish to emphasize the choice of a relevant set of experimental activities to provide data for the validation of computational models aiming to predict coronary stent deployment. Attention is focused on the use of ad hoc 3D-printed mock vessels in the validation plan, which could allow evaluating aspects of clinical relevance in a representative but controlled environment.

scholarly journals Detection of Black Hole Attack Using Honeypot Agent-Based Scheme with Deep Learning Technique on MANET

2021 ◽  
Vol 26 (6) ◽  
pp. 549-557
Author(s):  
Venkatasubramanian Srinivasan
Keyword(s):  
Black Hole ◽  
Intelligent Agents ◽  
Ad Hoc ◽  
Short Term Memory ◽  
Detection Methods ◽  
Delivery Ratio ◽  
Mobile Nodes ◽  
Black Hole Attack ◽  
Detection Scheme ◽  
Agent Based

Mobile Ad-Hoc Networks (MANETs) due to their reconfigurable nature are being integrated into new and futuristic knowledge such as Internet of Things (IoT), cloud, reconfigurable networks, etc. To attain such credibility of integration, the routing protocols associated with these mobile nodes have to connect, perform and facilitate routing that offers a high level of security and resistance to all possible threats and security issues that may emanate in the network. One of the solutions used to maintain network security is intrusion detection systems (IDSs). This article primarily emphasis on the network's susceptibility to a suction assault known as a black hole attack. The investigations about the employment of intelligent agents called Honeypot Agent-based detection scheme (HPAS) with Long-Short Term Memory (LSTM) in identifying such assaults. Hence, the proposed method is named HPAS-LSTM, where honeypots are roaming virtual software managers that create Route Request (RREQ) packets to attract and entrap black hole attackers. Extensive model results utilizing the ns-2 simulator are used to demonstrate the presence of the suggested detection technique. The simulation outcomes demonstrate that the suggested technique outperforms current black hole detection methods in terms of throughput (TH), packet loss rate (PLR), packet delivery ratio (PDR), and total network delay (TND).

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