Symbolic Communication in Artificial Creatures: An Experiment in Artificial Life

Loula, Ângelo; Gudwin, Ricardo; Queiroz, João

doi:10.1007/978-3-540-28645-5_34

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…After a while, no more new alarms are created since every prey already knows at least one alarm for each predator. After many iterations of this process, creatures begin to engage in symbol-based communication, which is characterized by faster responses, as previously verified in the specific case of tutor/apprentice populations , Loula et al 2004). The symbolic threshold is reached when the association between alarm and predator gets near maximum, leading to the consistent use of that association and eventually to the direct activation of the fleeing behavior, without the need to visually search for a predator.…”

Section: Simulations Of Interactions Among Creaturessupporting

confidence: 52%

“…A vocalization immediately becomes available to other prey by way of audition, raising their arousal and increasing their chance to evade predation. In the present work, prey were not initially divided into apprentices and tutors, as seen in the contrast between infant and adult vervet monkeys, and as we have previously simulated (Loula et al 2004). Rather, we sought to generalize the vervet monkey case of symbol-emergence by investigating a tabula rasa scenario in which no previous repertoire of alarms calls is known, and no symbol-based communication occurs yet.…”

Section: An Experiments On Symbol-based Communication Emergencementioning

confidence: 99%

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Brain Inspired Cognitive Systems 2008

Hussain¹,

Aleksander²,

Smith³

et al. 2010

Advances in Experimental Medicine and Biology

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The most unacceptable structural damage of porous asphalt top layers is the loss of stones leading to raveling. Therefore it is important to predict when the porous asphalt top layer will achieve a critical level of raveling so as to allocate funds for necessary maintenance. SHRP-NL database including eight provinces of the Netherlands was used as the data resource. Artificial Neural Network (ANN) was employed to predict severity of raveling having input parameters related to historical raveling and climate, construction and traffic factors. An ANN is able to forecast raveling low with a high correlation factor (R 2 =0.986), raveling moderate with ( R 2 =0.926) and raveling high with (R 2 =0.976). Besides another ANN provided sensitivity analysis indicating the relative contribution of factors related to climate (58%), traffic factor (14%), thickness (6%), roughness (12%) and age (10%) for raveling low and high but for raveling moderate climate (46%), traffic factor (15%), thickness (15%), roughness (13%) and age (11%) are the results. Color Contours illustrated that heavy traffic, low thickness and high roughness cause raveling on old asphalt especially in cold rainy days. ANN proved to be a powerful technique to predict and analyze raveling opening great opportunities for development of ANN models for other detriments.

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Section: Simulations Of Interactions Among Creaturessupporting

confidence: 52%

Section: An Experiments On Symbol-based Communication Emergencementioning

confidence: 99%

Brain Inspired Cognitive Systems 2008

Hussain¹,

Aleksander²,

Smith³

et al. 2010

Advances in Experimental Medicine and Biology

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“…To further explore the underlying mechanisms for the acquisition of alarm calls, we implemented an agent-based simulation of a prey-predator community inspired by the vervetmonkey case, as previously described elsewhere (Loula, Gudwin et al 2003;Loula, Gudwin et al 2004) (also see www.dca.fee.unicamp.br/projects/artcog/symbcreatures/). In our simulation, each creature acting in the virtual environment has an independent control system, being classified as either prey or predator.…”

Section: Methodsmentioning

confidence: 99%

Symbols are not uniquely human

Ribeiro¹,

Loula

Araújo³

et al. 2007

Biosystems

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Abstract. Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprenticegenerated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner.Tutor unreliability was also deleterious to vocal symbol learning: a mere 5% of "lying" tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices.Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax.

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“…This virtual world is composed of preys and predators (terrestrial, aerial and ground predators), and of things such as trees (climbable objects) and bushes (used to hide). The preys are not divided into apprentices and tutors, as with infants and adults in the vervet monkeys case and as we have previously simulated [19]. Here we go beyond the ethological case proposing a scenario in which no previous repertoire of alarms calls would be known and no symbolbased communication occurs yet.…”

Section: From Constraints To a Synthetic Experimentsmentioning

confidence: 96%