This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys.
This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs.
In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space.
Brain-inspired AGI development, in other words, cutting down the design space to look more like a biological brain, which is an existing model of a general intelligence, is a promising plan for solving this problem.
Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors.
Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.
Previous models disentangle static and dynamic factors by explicitly modeling the priors of latent variables to distinguish between these factors.
In cooperation, the workers must know how co-workers behave.
Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging.