Cloud Computing for Synergized Emotional Model Evolution in Multi-Agent Learning Systems

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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: 19 feb. 2020. 
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/
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%& 718
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%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.  19 Feb. 2020
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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	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)

Authors

Affiliations

  • Academy for Information Technology, University of Johannesburg, South Africa
  • Academy for Information Technology, University of Johannesburg, South Africa

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 56(2010)11, 718-727

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.

multi-agent learning; cognitive architecture; emotional models; scalability; intelligent agent; cloud computing;