Modulation Scheme for Biasing the Emotional Process of Autonomous Agents

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.

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

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2018100105 ◽

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.

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An Interoperable Framework for Computational Models of Emotion

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.296257 ◽

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

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Finding Topics in Email Using Formal Concept Analysis and Fuzzy Membership Functions

Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-540-68825-9_11 ◽

2008 ◽

pp. 108-113 ◽

Cited By ~ 1

Author(s):

Liqiang Geng ◽

Larry Korba ◽

Yunli Wang ◽

Xin Wang ◽

Yonghua You

Keyword(s):

Formal Concept Analysis ◽

Concept Analysis ◽

Fuzzy Membership ◽

Formal Concept ◽

Membership Functions ◽

Fuzzy Membership Functions

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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00354-9 ◽

2021 ◽

Author(s):

Jia-Bin Zhou ◽

Yan-Qin Bai ◽

Yan-Ru Guo ◽

Hai-Xiang Lin

Keyword(s):

Support Vector Machine ◽

Negative Impact ◽

Twin Support Vector Machine ◽

Fuzzy Membership ◽

Support Vector ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Intuitionistic Fuzzy ◽

Benchmark Datasets ◽

The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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Computational models of adaptive behavior and prefrontal cortex

Neuropsychopharmacology ◽

10.1038/s41386-021-01123-1 ◽

2021 ◽

Author(s):

Alireza Soltani ◽

Etienne Koechlin

Keyword(s):

Prefrontal Cortex ◽

Adaptive Behavior ◽

Computational Models ◽

Cognitive Architecture ◽

Behavioral Outcomes ◽

Internal Models ◽

Multiple Systems ◽

Statistical Inferences ◽

Neural Computations ◽

Task Sets

AbstractThe real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior.

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Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

Neurobiology of Language ◽

10.1162/nol_a_00018 ◽

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.

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