A Verification Method for MASOES

Perozo, Niriaska; Aguilar, José; Terán, Oswaldo; Molina, Heidy

doi:10.1109/tsmcb.2012.2199106

Cited by 30 publications

(10 citation statements)

References 23 publications

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“…These four requirements do not assure good quality of data, but they are the minimal characteristics required to test our approaches. No dataset with the four characteristics was found, some of the explored datasets were: IEMO-CAP, Enterface [42], Mosei [43], SEMAINE [44], MOSI [45], MOUD [46], MELD [47], Humaine [48,49], CK [50], CK+ [51], DEAP [52], AMIGOS [53,54], AFEW-VA [13]. Thus, it was decided to look for unimodal datasets that meet these requirements and as far as possible that they reflect the real conditions of virtual education, with the objective of fusing them for creating an artificial dataset that have the required features.…”

Section: Artificial Datasetmentioning

confidence: 99%

Analysis of different affective state multimodal recognition approaches with missing data-oriented to virtual learning environments

Salazar

Montoya-Múnera

Aguilar

2021

Heliyon

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In this work, the affective state of users in virtual learning environments is assessed/recognized in terms of continuous arousal and valence dimensions, making use of multimodal information (audio, text and video), whenever any of these modalities are available. In general, virtual learning environments where these three modalities are all the time, are not common; at some moments only the video modality is available, while in others only text or/and video and/or audio. Different approaches using feature-level fusion and decision-level fusion are proposed for multimodal recognition with missing data. Recognizing according to available modalities is studied following the ideas of dropout from neural networks and of variable input length from recurrent neural networks. This proposal is innovative because it represents emotions in the continuous space, which is not common in virtual education; and makes use of the available modalities in a virtual environment in a given moment, which is very common in virtual learning environments because the people are not speaking or writing all the time.

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Section: Artificial Datasetmentioning

confidence: 99%

Analysis of different affective state multimodal recognition approaches with missing data-oriented to virtual learning environments

Salazar

Montoya-Múnera

Aguilar

2021

Heliyon

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“…Emergence [2,8,16,17] is "what happens when a relatively simple system of elements organizes itself spontaneously, without explicit laws, and thus gives rise to intelligent behavior." In the literature, there are several definitions of emergent games.…”

Section: Emerging Serious Gamesmentioning

confidence: 99%

Metrópolis: un juego serio emergente en una ciudad inteligente

Aguilar

Serna

Altamiranda

et al. 2019

DYNA

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Emerging serious games mark the beginning of a new era in video games. Emerging serious games introduce advances in distributed artificial intelligence into their design in order to guide the player’s manipulation of a specific subject in an adaptive way. In this article, we present a city simulator game, called Metropolis, which generates emergent properties. Metropolis can be used by a smart city for city planning, to make collective decisions, and for other purposes. This paper describes why Metropolis can be classified as a serious game. It also analyzes how its emergent properties can be used for managing a smart city, and especially how it promotes e-participation as an e-decision-making tool within the context of urban planning. In addition, this paper explores how Metropolis can be used to analyze a smart city’s emergent citizen and urban patterns (urban spatial distribution) based on strong e-participation.

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“…The model seeks to provide a simplified emotional spectrum to the robots that act in the system, in order to affect the way how they conduct their behaviors, which also affects the collective behavior. These three types of behavior (imitative, cognitive and reactive) will be associated to an emotional state, like has been proposed in [31,32]. We use the same assumptions of these works: the negative emotions predispose the individual to the resolution problem using an individual approach, while the positive emotions lead the individual to global goals, using a collective approach.…”

Section: Our Emotional Modelmentioning

confidence: 99%

“…In order to validate the influence of the emotions in the emergent behavior of the multi-robot system, we consider the verification method proposed in [32], called MASOES. This work proposes a set of concepts related to emerging properties, which include the emotiveness, the emotion types and the behavior component.…”

Section: Emergent Behavior Analysis Based On the Masoes Modelmentioning

confidence: 99%

“…These concepts are determined by the changes in the emotional states, the types of emotions in the individuals in the system, and the existence of a component in the system that determines its behavior according to the internal states as the emotions of the individuals. In the verification method proposed in [32], these concepts are part of a set of architectural concepts defined in a Fuzzy Cognitive Map (FCM) [26,38,41,42], which has been implemented in the FCM Designer Tool [45]. The fuzzy cognitive map verifies the emergent behavior of a system, according to the characteristics of the architectural concepts in the system [32].…”

Section: Emergent Behavior Analysis Based On the Masoes Modelmentioning

confidence: 99%

See 1 more Smart Citation

An emotional model for swarm robotics

Gil¹,

Puerto²,

Aguilar³

et al. 2019

CLEIej

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Swarm robotics is a system of multiple robots where a desired collective behavior emerges from the interactions between the robots and with the environment. This paper proposes an emotional model for the robots, to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions. The more positive the robot is in its emotion, the more it will consider the group and global goals. Each robot is able to recognize another robot's emotion in the system based on their current state, using the AR2P recognition algorithm. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper analyses two emergent scenarios: nectar harvesting and object transportation, and shows the importance of the emotions into the emergent behavior in a multi-robot system

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A Verification Method for MASOES

Cited by 30 publications

References 23 publications

Analysis of different affective state multimodal recognition approaches with missing data-oriented to virtual learning environments

Analysis of different affective state multimodal recognition approaches with missing data-oriented to virtual learning environments

Metrópolis: un juego serio emergente en una ciudad inteligente

An emotional model for swarm robotics

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