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Found 4 papers, 2 papers with code
Goal-directed Planning and Goal Understanding by Active Inference: Evaluation Through Simulated and Physical Robot Experiments
We show that goal-directed action planning and generation in a teleological framework can be formulated using the free energy principle.
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences.
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory
Furthermore, our analysis of comparative experiments indicated that introduction of visual working memory and the inference mechanism using variational Bayes predictive coding significantly improve the performance in planning adequate goal-directed actions.
Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model
The current paper presents how a predictive coding type deep recurrent neural networks can generate vision-based goal-directed plans based on prior learning experience by examining experiment results using a real arm robot.
