Cloud computing for synergized emotional model evolution in multi-agent learning systems

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.