Affective multi-agent system for simulating mechanisms of social effects of emotions

Pudāne, Māra

doi:10.1109/aciiw.2017.8272602

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…They receive rewards from the environment as a consequence of their actions, computing sensations through an internal environment composed of emotion appraisal and derivation models that generate intrinsic rewards useful for behavioral adaptation. Furthermore, affective interaction mechanisms have also been studied in [27]: social effects of emotions are classified primarily as emotions experienced but not communicated, emotions experienced and intentionally communicated, and emotions not experienced but intentionally communicated. Starting from a psychological background, they have realized a multi-agent system in which competitive and cooperative interactions have been obtained between agents with negative and positive social connections, respectively.…”

Section: Related Workmentioning

confidence: 99%

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

Iovane

Fominska²,

Landi

et al. 2020

Electronics

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This study explores an info-structural model of cognition for non-interacting agents affected by human sensation, perception, emotion, and affection. We do not analyze the neuroscientific or psychological debate concerning the human mind working, but we underline the importance of modeling the above cognitive levels when designing artificial intelligence agents. Our aim was to start a reflection on the computational reproduction of intelligence, providing a methodological approach through which the aforementioned human factors in autonomous systems are enhanced. The presented model must be intended as part of a larger one, which also includes concepts of attention, awareness, and consciousness. Experiments have been performed by providing visual stimuli to the proposed model, coupling the emotion cognitive level with a supervised learner to produce artificial emotional activity. For this purpose, performances with Random Forest and XGBoost have been compared and, with the latter algorithm, 85% accuracy and 92% coherency over predefined emotional episodes have been achieved. The model has also been tested on emotional episodes that are different from those related to the training phase, and a decrease in accuracy and coherency has been observed. Furthermore, by decreasing the weight related to the emotion cognitive instances, the model reaches the same performances recorded during the evaluation phase. In general, the framework achieves a first emotional generalization responsiveness of 94% and presents an approximately constant relative frequency related to the agent’s displayed emotions.

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Section: Related Workmentioning

confidence: 99%

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

Iovane

Fominska²,

Landi

et al. 2020

Electronics

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“…These models are much less researched from the psychological as well as affective computing perspective. While there are several developments that study the behaviour of the crowd (see e.g., [39]), much less work exists on emotions in smaller groups [40]. Thus, modelling of emotion communication mechanisms at large remains a challenge.…”

Section: Modelling Of Artificial Peersmentioning

confidence: 99%

Challenges in the Development of Affective Collaborative Learning Environment with Artificial Peers

Pudāne

Petroviča

Lavendelis

et al. 2018

Applied Computer Systems

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Collaborative learning is a process that involves a group of peers collaborating with the aim to acquire new knowledge or skills. Collaborative learning environment enables such interactions by means of ICT. The paper focuses on affective collaborative learning environments, i.e., collaborative learning environments that are additionally aware of user’s emotions and moods. Based on the analysis of existing research, a general architecture of an affective collaborative learning environment has been proposed in the paper and the main challenges for developing such an environment have been identified, namely, nonintrusive and safe detection of user’s emotions, the adaptation of tutoring strategies, as well as modelling of artificial peers. This study can be considered the first step for the development of the collaborative learning environment that takes into account various affective aspects during the collaborative learning process.

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“…The interaction among affective agents includes not only rational interactions and agent's reactions towards them but also affective interactions, i.e., specific patterns that ensure the transfer of emotions. There are 5 possible affective interaction scenarios among agents [26] that include subconscious mechanisms from the perspective of a sender (i.e., messages that are semantically sent with no purpose of sending message) and conscious mechanisms, namely, direct communication and manipulation.…”

Section: A Macro Level Design Of Affective Masmentioning

confidence: 99%

“…Affective interactions differ from rational interactions since the interpretation is ambiguous. As the interpretation of emotion cues lies within a receiver [26], changes concerning interpretation will be done at the micro level.…”

Section: A Macro Level Design Of Affective Masmentioning

confidence: 99%

General Guidelines for Design of Affective Multi-Agent Systems

Pudāne

Lavendelis

2017

Applied Computer Systems

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-The paper presents general guidelines for designing affective multi-agent systems (affective MASs). The guidelines aim at extending the existing agent-oriented software engineering (AOSE) methodologies to enable them to design affective MASs. The reason why affective mechanisms need specific attention during the design is the fact that the way how both rational tasks and interactions are done differ based on the affective state of the agents. Thus, the paper extends the traditional design approaches with the design of affective mechanisms and includes them in the design of the system as a whole.

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Affective multi-agent system for simulating mechanisms of social effects of emotions

Cited by 5 publications

References 11 publications

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

Smart Sensing: An Info-Structural Model of Cognition for Non-Interacting Agents

Challenges in the Development of Affective Collaborative Learning Environment with Artificial Peers

General Guidelines for Design of Affective Multi-Agent Systems

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