Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

Sendari, Siti; Afandi, Arif Nur; Zaeni, Ilham Ari Elbaith; Mahandi, Yogi Dwi; Hirasawa, Kotaro; Lin, Hsien-I

doi:10.12928/telkomnika.v17i3.12232

Cited by 5 publications

(3 citation statements)

References 18 publications

(30 reference statements)

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“…Before RL can be explained, it necessary to understand basic components used. A learner called an agent in RL studies its behavior by select actions in an environment [25]. At each time, the agent receives a representation of state , while ∈ , where is states.…”

Section: Dialogue Managementmentioning

confidence: 99%

Dialogue management using reinforcement learning

2021

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Dialogue has been widely used for verbal communication between human and robot interaction, such as assistant robot in hospital. However, this robot was usually limited by predetermined dialogue, so it will be difficult to understand new words for new desired goal. In this paper, we discussed conversation in Indonesian on entertainment, motivation, emergency, and helping with knowledge growing method. We provided mp3 audio for music, fairy tale, comedy request, and motivation. The execution time for this request was 3.74 ms on average. In emergency situation, patient able to ask robot to call the nurse. Robot will record complaint of pain and inform nurse. From 7 emergency reports, all complaints were successfully saved on database. In helping conversation, robot will walk to pick up belongings of patient. Once the robot did not understand with patient's conversation, robot will ask until it understands. From asking conversation, knowledge expands from 2 to 10, with learning execution from 1405 ms to 3490 ms. SARSA was faster towards steady state because of higher cumulative rewards. Q-learning and SARSA were achieved desired object within 200 episodes. It concludes that reinforcement learning (RL) method to overcome robot knowledge limitation in achieving new dialogue goal for patient assistant were achieved.

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Section: Dialogue Managementmentioning

confidence: 99%

Dialogue management using reinforcement learning

2021

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“…For both practitioners and scholars working with aviation applications, state-of-the-art solutions for transportation planning combining baggage services, routing, security, and safety are an expanding subject (Nguyen and Teague, 2015;Sendari et al, 2019). With the goal of contributing to improving service quality, reducing work pressure for staff at airports when the number of passengers is being considered, especially by providing flight-related information, along with other necessary information for passengers conveniently and quickly (Joosse et al, 2015;Triebel et al, 2016;Ivanov et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A novel design of a smart interactive guiding robot for busy airports

Tran

Nguyen

et al. 2022

International Journal on Smart Sensing and Intelligent Systems

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The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.

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“…Data mining techniques provide insight from data relationships and provide researchers and users with rules for classification, association [5]- [7], and prediction. The use of genetic programming in solving classification problems is relatively frequent [8]- [11]. In this case, these are supervised learning algorithms where individuals in the population represent a classifier in whole or in part, and their evaluation measures the ability to correctly classify a dataset that has been externally evaluated [12].…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Approach for Classification Problem using GPU

Santoso

2020

EECSI

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Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in parallel.

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Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

Cited by 5 publications

References 18 publications

Dialogue management using reinforcement learning

Dialogue management using reinforcement learning

A novel design of a smart interactive guiding robot for busy airports

Genetic Programming Approach for Classification Problem using GPU

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