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Found 11 papers, 1 papers with code
DEQ-MCL: Discrete-Event Queue-based Monte-Carlo Localization
Spatial cognition in hippocampal formation is posited to play a crucial role in the development of self-localization techniques for robots.
Recognition of All Categories of Entities by AI
Human-level AI will have significant impacts on human society.
Brain-inspired probabilistic generative model for double articulation analysis of spoken language
This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys.
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots
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.
Hippocampal formation-inspired probabilistic generative model
In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space.
The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain
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.
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
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 Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.
FAVAE: Sequence Disentanglement using Information Bottleneck Principle
Previous models disentangle static and dynamic factors by explicitly modeling the priors of latent variables to distinguish between these factors.
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents
In cooperation, the workers must know how co-workers behave.
Bayesian Inference of Self-intention Attributed by Observer
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.
