JERBIĆ, Bojan ;VRANJEŠ, Božo . A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 49, n.1, p. 52-62, july 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/a-study-of-a-robotic-assembly-system-as-a-collaborative-multi-agent-organization/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/.
Jerbić, B., & Vranješ, B. (2003). A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization. Strojniški vestnik - Journal of Mechanical Engineering, 49(1), 52-62. doi:http://dx.doi.org/
@article{.,
author = {Bojan Jerbić and Božo Vranješ},
title = {A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {49},
number = {1},
year = {2003},
keywords = {robotic assembly; multiagent systems; autonomous agent; learning methods; neural networks; },
abstract = {This paper looks at designing a robotic assembly system as a multi-agent system. Any multi-device system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability to create a collaborative system that can provide the achieving of the social intelligence. Social behavior is the highest form of intelligence, which is able to solve very complex problems, autonomously create new procedures and efficiently adapt to new tasks. The presented multi-agent model is based on processing units that include recognition networks, problem-solving strategies and learning engines. It integrates perception, recognition, problem-solving, learning and communication capabilities. The reinforcement learning method is used here to evaluate the robot is behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience, which can be used in solving similar problems in the future. To recognize problem similarities we applied the Adaptive Fuzzy Shadowed (AFS) neural network.},
issn = {0039-2480}, pages = {52-62}, doi = {},
url = {https://www.sv-jme.eu/sl/article/a-study-of-a-robotic-assembly-system-as-a-collaborative-multi-agent-organization/}
}
Jerbić, B.,Vranješ, B. 2003 July 49. A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 49:1
%A Jerbić, Bojan %A Vranješ, Božo %D 2003 %T A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization %B 2003 %9 robotic assembly; multiagent systems; autonomous agent; learning methods; neural networks; %! A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization %K robotic assembly; multiagent systems; autonomous agent; learning methods; neural networks; %X This paper looks at designing a robotic assembly system as a multi-agent system. Any multi-device system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability to create a collaborative system that can provide the achieving of the social intelligence. Social behavior is the highest form of intelligence, which is able to solve very complex problems, autonomously create new procedures and efficiently adapt to new tasks. The presented multi-agent model is based on processing units that include recognition networks, problem-solving strategies and learning engines. It integrates perception, recognition, problem-solving, learning and communication capabilities. The reinforcement learning method is used here to evaluate the robot is behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience, which can be used in solving similar problems in the future. To recognize problem similarities we applied the Adaptive Fuzzy Shadowed (AFS) neural network. %U https://www.sv-jme.eu/sl/article/a-study-of-a-robotic-assembly-system-as-a-collaborative-multi-agent-organization/ %0 Journal Article %R %& 52 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 49 %N 1 %@ 0039-2480 %8 2017-07-07 %7 2017-07-07
Jerbić, Bojan, & Božo Vranješ. "A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization." Strojniški vestnik - Journal of Mechanical Engineering [Online], 49.1 (2003): 52-62. Web. 20 Dec. 2024
TY - JOUR AU - Jerbić, Bojan AU - Vranješ, Božo PY - 2003 TI - A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - robotic assembly; multiagent systems; autonomous agent; learning methods; neural networks; N2 - This paper looks at designing a robotic assembly system as a multi-agent system. Any multi-device system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability to create a collaborative system that can provide the achieving of the social intelligence. Social behavior is the highest form of intelligence, which is able to solve very complex problems, autonomously create new procedures and efficiently adapt to new tasks. The presented multi-agent model is based on processing units that include recognition networks, problem-solving strategies and learning engines. It integrates perception, recognition, problem-solving, learning and communication capabilities. The reinforcement learning method is used here to evaluate the robot is behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience, which can be used in solving similar problems in the future. To recognize problem similarities we applied the Adaptive Fuzzy Shadowed (AFS) neural network. UR - https://www.sv-jme.eu/sl/article/a-study-of-a-robotic-assembly-system-as-a-collaborative-multi-agent-organization/
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author = {Jerbić, B., Vranješ, B.},
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volume = {49},
number = {1},
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TY - JOUR AU - Jerbić, Bojan AU - Vranješ, Božo PY - 2017/07/07 TI - A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 49, No 1 (2003): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - robotic assembly, multiagent systems, autonomous agent, learning methods, neural networks, N2 - This paper looks at designing a robotic assembly system as a multi-agent system. Any multi-device system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability to create a collaborative system that can provide the achieving of the social intelligence. Social behavior is the highest form of intelligence, which is able to solve very complex problems, autonomously create new procedures and efficiently adapt to new tasks. The presented multi-agent model is based on processing units that include recognition networks, problem-solving strategies and learning engines. It integrates perception, recognition, problem-solving, learning and communication capabilities. The reinforcement learning method is used here to evaluate the robot is behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience, which can be used in solving similar problems in the future. To recognize problem similarities we applied the Adaptive Fuzzy Shadowed (AFS) neural network. UR - https://www.sv-jme.eu/sl/article/a-study-of-a-robotic-assembly-system-as-a-collaborative-multi-agent-organization/
Jerbić, Bojan, AND Vranješ, Božo. "A Study of a Robotic Assembly System as a Collaborative Multi-Agent Organization" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 49 Number 1 (07 July 2017)
Strojniški vestnik - Journal of Mechanical Engineering 49(2003)1, 52-62
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper looks at designing a robotic assembly system as a multi-agent system. Any multi-device system, or any system whose performance is naturally decomposable, can be interpreted as a corporation of agents. Such a scheme comprises the ability to create a collaborative system that can provide the achieving of the social intelligence. Social behavior is the highest form of intelligence, which is able to solve very complex problems, autonomously create new procedures and efficiently adapt to new tasks. The presented multi-agent model is based on processing units that include recognition networks, problem-solving strategies and learning engines. It integrates perception, recognition, problem-solving, learning and communication capabilities. The reinforcement learning method is used here to evaluate the robot is behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience, which can be used in solving similar problems in the future. To recognize problem similarities we applied the Adaptive Fuzzy Shadowed (AFS) neural network.