An Attention-Based Predictive Agent for Static and Dynamic Environments

doi:10.1109/ACCESS.2022.3149585

An Attention-Based Predictive Agent for Static and Dynamic Environments

2022-01-01

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AI

DOI：10.1109/ACCESS.2022.3149585

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Real-world applications of intelligent agents demand accuracy and efficiency, and seldom provide reinforcement signals. Currently, most agent models are reinforcement-based and concentrate exclusively on accuracy. We propose a general-purpose agent model consisting of proprioceptive and perceptual pathways. The agent actively samples its environment via a sequence of glimpses. It completes the partial propriocept and percept sequences observed till each sampling instant, and learns where and what to sample by minimizing prediction error, without reinforcement or supervision (class labels). The model is evaluated by exposing it to two kinds of stimuli: images of fully-formed handwritten numerals and alphabets, and videos of gradual formation of numerals. It yields state-of-the-art prediction accuracy upon sampling only 22.6% of the scene on average. The model saccades when exposed to images and tracks when exposed to videos. This is the first known attention-based agent to generate realistic handwriting with state-of-the-art accuracy and efficiency by interacting with and learning end-to-end from static and dynamic environments.

Keywords:

Predictive models

Videos

Visualization

Data models

Computational modeling

Decoding

Mathematical models

Agent

attention

handwriting generation

multimodal

perception

proprioception

An Attention-Based Predictive Agent for Static and Dynamic Environments

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