Positive affectivity, or the characteristic that describes how people experience affects (e.g., sensations, emotions, and sentiments) and interact with others as a result, has been linked to increased interest and curiosity as well as satisfaction in learning. Inspired by this, a team of Microsoft researchers propose imbuing reinforcement learning, an AI training technique that employs rewards to spur systems toward goals, with positive affect, which they assert might drive exploration useful in gathering experiences critical to learning. From a report: As the researchers explain, reinforcement learning is commonly implemented via policy-specific rewards designed for a predefined goal. Problematically, these extrinsic rewards are narrow in scope and can be difficult to define, as opposed to intrinsic rewards that are task-independent and quickly indicate success or failure. In pursuit of an intrinsic policy, the researchers developed a framework comprising mechanisms motivated by human affect — one that motivates agents by drives like delight. Using a computer vision system that models the reward and another system that uses data to solve multiple tasks, it measures human smiles as positive affect. The framework encourages agents to explore virtual or real-world environments without getting into perilous situations, and it has the advantage of being agnostic to any specific machine intelligence application. A positive intrinsic reward mechanism predicts human smile responses as the exploration evolves, while a sequential decision-making framework learns a generalizable policy. As for the positive intrinsic affect model, it changes the action selection such that it biases actions providing better intrinsic rewards, and a final component uses data collected during the agent’s exploration to build representations for visual recognition and understanding tasks.
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Source:: Slashdot