BARNETT, Tristan ;EHLERS, Elizabeth . Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 56, n.11, p. 718-727, october 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/cloud-computing-for-synergized-emotional-model-evolution-in-multi-agent-learning-systems/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/.
Barnett, T., & Ehlers, E. (2010). Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems. Strojniški vestnik - Journal of Mechanical Engineering, 56(11), 718-727. doi:http://dx.doi.org/
@article{.,
author = {Tristan Barnett and Elizabeth Ehlers},
title = {Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {56},
number = {11},
year = {2010},
keywords = {multi-agent learning; cognitive architecture; emotional models; scalability; intelligent agent; cloud computing; },
abstract = {Machine learning is a technology paramount to enhancing the adaptability of agent-based systems. Learning is a desirable aspect in synthetic characters, or ‘believable’ agents, as it offers a degree of realism to their interactions. However, the advantage of collaborative efforts in multi-agent learning systems can be overshadowed by concerns over system scalability and adaptive dynamics. The proposed Multi-Agent Learning through Distributed Artificial Consciousness (MALDAC) Architecture is proposed as a scalable approach to developing adaptable systems in complex, believable environments. To support MALDAC, a cognitive architecture is proposed which applies emotional models and artificial consciousness theory to cope with complex environments. Furthermore, the cloud computing paradigm is employed in the architecture’s design to enhance system scalability. A virtual environment implementing MALDAC is shown to enhance scalability in multi-agent learning systems, particularly in stochastic and dynamic environments.},
issn = {0039-2480}, pages = {718-727}, doi = {},
url = {https://www.sv-jme.eu/article/cloud-computing-for-synergized-emotional-model-evolution-in-multi-agent-learning-systems/}
}
Barnett, T.,Ehlers, E. 2010 October 56. Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 56:11
%A Barnett, Tristan %A Ehlers, Elizabeth %D 2010 %T Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems %B 2010 %9 multi-agent learning; cognitive architecture; emotional models; scalability; intelligent agent; cloud computing; %! Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems %K multi-agent learning; cognitive architecture; emotional models; scalability; intelligent agent; cloud computing; %X Machine learning is a technology paramount to enhancing the adaptability of agent-based systems. Learning is a desirable aspect in synthetic characters, or ‘believable’ agents, as it offers a degree of realism to their interactions. However, the advantage of collaborative efforts in multi-agent learning systems can be overshadowed by concerns over system scalability and adaptive dynamics. The proposed Multi-Agent Learning through Distributed Artificial Consciousness (MALDAC) Architecture is proposed as a scalable approach to developing adaptable systems in complex, believable environments. To support MALDAC, a cognitive architecture is proposed which applies emotional models and artificial consciousness theory to cope with complex environments. Furthermore, the cloud computing paradigm is employed in the architecture’s design to enhance system scalability. A virtual environment implementing MALDAC is shown to enhance scalability in multi-agent learning systems, particularly in stochastic and dynamic environments. %U https://www.sv-jme.eu/article/cloud-computing-for-synergized-emotional-model-evolution-in-multi-agent-learning-systems/ %0 Journal Article %R %& 718 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 56 %N 11 %@ 0039-2480 %8 2017-10-24 %7 2017-10-24
Barnett, Tristan, & Elizabeth Ehlers. "Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems." Strojniški vestnik - Journal of Mechanical Engineering [Online], 56.11 (2010): 718-727. Web. 20 Dec. 2024
TY - JOUR AU - Barnett, Tristan AU - Ehlers, Elizabeth PY - 2010 TI - Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - multi-agent learning; cognitive architecture; emotional models; scalability; intelligent agent; cloud computing; N2 - Machine learning is a technology paramount to enhancing the adaptability of agent-based systems. Learning is a desirable aspect in synthetic characters, or ‘believable’ agents, as it offers a degree of realism to their interactions. However, the advantage of collaborative efforts in multi-agent learning systems can be overshadowed by concerns over system scalability and adaptive dynamics. The proposed Multi-Agent Learning through Distributed Artificial Consciousness (MALDAC) Architecture is proposed as a scalable approach to developing adaptable systems in complex, believable environments. To support MALDAC, a cognitive architecture is proposed which applies emotional models and artificial consciousness theory to cope with complex environments. Furthermore, the cloud computing paradigm is employed in the architecture’s design to enhance system scalability. A virtual environment implementing MALDAC is shown to enhance scalability in multi-agent learning systems, particularly in stochastic and dynamic environments. UR - https://www.sv-jme.eu/article/cloud-computing-for-synergized-emotional-model-evolution-in-multi-agent-learning-systems/
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author = {Barnett, T., Ehlers, E.},
title = {Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {56},
number = {11},
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TY - JOUR AU - Barnett, Tristan AU - Ehlers, Elizabeth PY - 2017/10/24 TI - Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 56, No 11 (2010): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - multi-agent learning, cognitive architecture, emotional models, scalability, intelligent agent, cloud computing, N2 - Machine learning is a technology paramount to enhancing the adaptability of agent-based systems. Learning is a desirable aspect in synthetic characters, or ‘believable’ agents, as it offers a degree of realism to their interactions. However, the advantage of collaborative efforts in multi-agent learning systems can be overshadowed by concerns over system scalability and adaptive dynamics. The proposed Multi-Agent Learning through Distributed Artificial Consciousness (MALDAC) Architecture is proposed as a scalable approach to developing adaptable systems in complex, believable environments. To support MALDAC, a cognitive architecture is proposed which applies emotional models and artificial consciousness theory to cope with complex environments. Furthermore, the cloud computing paradigm is employed in the architecture’s design to enhance system scalability. A virtual environment implementing MALDAC is shown to enhance scalability in multi-agent learning systems, particularly in stochastic and dynamic environments. UR - https://www.sv-jme.eu/article/cloud-computing-for-synergized-emotional-model-evolution-in-multi-agent-learning-systems/
Barnett, Tristan, AND Ehlers, Elizabeth. "Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 56 Number 11 (24 October 2017)
Strojniški vestnik - Journal of Mechanical Engineering 56(2010)11, 718-727
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Machine learning is a technology paramount to enhancing the adaptability of agent-based systems. Learning is a desirable aspect in synthetic characters, or ‘believable’ agents, as it offers a degree of realism to their interactions. However, the advantage of collaborative efforts in multi-agent learning systems can be overshadowed by concerns over system scalability and adaptive dynamics. The proposed Multi-Agent Learning through Distributed Artificial Consciousness (MALDAC) Architecture is proposed as a scalable approach to developing adaptable systems in complex, believable environments. To support MALDAC, a cognitive architecture is proposed which applies emotional models and artificial consciousness theory to cope with complex environments. Furthermore, the cloud computing paradigm is employed in the architecture’s design to enhance system scalability. A virtual environment implementing MALDAC is shown to enhance scalability in multi-agent learning systems, particularly in stochastic and dynamic environments.