Behavioral task processing for cognitive robots using artificial emotions

Dağlarlı, Evren; Temeltaş, Hakan; Yesiloglu, S.M.

doi:10.1016/j.neucom.2008.07.018

Cited by 19 publications

(8 citation statements)

References 3 publications

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“…One type of the cognitive architectures is based on emotions [1,11,12]. Different research groups [6,13,18] are focusing on the design of the control architectures for emotionalbased robots.…”

Section: Related Workmentioning

confidence: 99%

“…(ii) As an emotional response, the robot generates new desires (11), and motivates the behavior to accomplish the desire (13). The desires are the result of application emotions, and their response functions (10) depend on the problem, meanwhile the motivations are the result of intrinsic emotions, and their response functions (12) depend of the character of the robot.…”

Section: Emotional Processes Specificationmentioning

confidence: 99%

“…They are becoming promising solutions for the implementation of advanced robotic systems [3,[9][10][11][12] because they facilitate the process of decision-making [1,13]. They use the mechanism of emotion in organizing the behaviors, which has the following advantages: allow the robot to be autonomous to focus its attention on the most promising behavior; the emotional motivation processes, being reactive processes, has a bounded response time, and help in the organization of the deliberative processes, which in general have not a bounded response time [14,15]; sort the problems based on the expectations of success; autonomously adapt the computational load to the available processor capacity, allowing solving problems of increasing complexity; separate the decision from the action processes; and use of subjective appraisal of the situation permits finding always an alternative solution.…”

Section: Introductionmentioning

confidence: 99%

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Embedded GPU and multicore processors for emotional-based mobile robotic agents

Almenar

Domínguez

Hassan

et al. 2016

Future Generation Computer Systems

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h i g h l i g h t s• We present alternatives to accelerate the behavior motivation's processes of emotional robotic agents.• We compare three accelerators based on GPUs, Multicores and SIMD-instructions (SSE and AVX) to a base single-core implementation.• We have simulated a mobile robotic application to compare the accelerating alternatives. a b s t r a c tControl architectures based on emotions are becoming promising solutions for the implementation of future robotic systems. The basic controllers of this architecture are the emotional processes that decide which behaviors the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, limiting the processing capacity of a main processor to solve the complex problems. Fortunately, the potential parallelism of emotional processes permits their execution in parallel, hence enabling the computing power to tackle the complex dynamic problems. In this paper, Graphic Processing Unit (GPU), multicore processors and single instruction multiple data (SIMD) instructions are used to provide parallelism for the emotional processes. Different GPUs, multicore processors and SIMD instruction sets are evaluated and compared to analyze their suitability to cope with robotic applications. The applications are set-up taking into account different environmental conditions, robot dynamics and emotional states. Experimental results show that, despite the fact that GPUs have a bottleneck in the data transmission between the host and the device, the evaluated GTX 670 GPU provides a performance of more than one order of magnitude greater than the initial implementation of the architecture on a single core. Thus, all complex proposed application problems can be solved using the GPU technology in contrast to the first prototype where only 55% of them could be solved. Using AVX SIMD instructions, the performance of the architecture is increased in 3.25 times in relation to the first implementation. Thus, from the 27 proposed applications about 88.8% are solved. In the case of the SSE SIMD instructions, the performance is almost doubled and the robot could solve about 74% of the proposed application problems. The use of AVX and SSE SIMD instructions provides almost the same performance as a quad-and a dual-core, respectively, with the advantage that they do not add any additional hardware cost.

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Section: Related Workmentioning

confidence: 99%

Section: Emotional Processes Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Embedded GPU and multicore processors for emotional-based mobile robotic agents

Almenar

Domínguez

Hassan

et al. 2016

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Generally, people express their feelings such that more euphemistic and keyword detection methods are valid for a specific corpus only. Classification and regression models include support vector machines (SVMs) [5], [6], Bayesian reasoning [7], [8], and artificial neural networks (ANNs) [9]. By training a classifier with a corpus as input, the sentiment intensity of a keyword can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Transfer Correlation Between Textual Content to Images for Sentiment Analysis

et al. 2020

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In social media, images and texts are used to convey individuals' attitudes and feelings; thus, social media has become an indispensable part of people's lives. To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful. One sentiment analysis task is polarity prediction. Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage. To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity. This method transfers the correlation between textual content to images. First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD). Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU). Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity. Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach. Moreover, extensive experimental results show that our model outperforms baseline solutions. INDEX TERMS Correlation, cross-modal, transfer, sentiment analysis.

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“…There exists much literature on the study which focuses on the concept of emotions that animals have, for developing autonomous systems in various fields [1][2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

A Decision Making System of Robots Introducing a Re-construction of Emotions Based on Their Own Experiences

Watada¹,

Obayashi²,

Kuremoto³

et al. 2014

JRNAL

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We already proposed a decision making system for autonomous robots, which is based on a Markovian emotional model proposed by Banik. In our proposed system, decision making of the robot is designed automatically by two processes that are the construction of emotions and optimization of behavior selection policy for emotions. In this study, we introduce an experiences-based reconstruction of emotions to our decision making system as an approach to an automatic construction of decision making online learning.

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Behavioral task processing for cognitive robots using artificial emotions

Cited by 19 publications

References 3 publications

Embedded GPU and multicore processors for emotional-based mobile robotic agents

Embedded GPU and multicore processors for emotional-based mobile robotic agents

Transfer Correlation Between Textual Content to Images for Sentiment Analysis

A Decision Making System of Robots Introducing a Re-construction of Emotions Based on Their Own Experiences

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