Introspective Agents: Confidence Measures for General Value Functions

TL;DR

This paper proposes that intelligent agents should internally predict their own learning confidence using general value functions, enabling better decision-making in dynamic environments.

Contribution

It introduces the idea of encoding confidence measures as internal predictions within general value functions for more adaptive agents.

Findings

Conceptual framework for confidence as internal prediction

Potential benefits for decision-making in changing environments

Extension of general value functions to include internal signals

Abstract

Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions. While such adaptive agents may leverage engineered knowledge, they will require the capacity to construct and evaluate knowledge themselves from their own experience in a bottom-up, constructivist fashion. This position paper builds on the idea of encoding knowledge as temporally extended predictions through the use of general value functions. Prior work has focused on learning predictions about externally derived signals about a task or environment (e.g. battery level, joint position). Here we advocate that the agent should also predict internally generated signals regarding its own learning process - for example, an agent's confidence in its learned predictions. Finally, we suggest how such information would be beneficial in creating an introspective agent that is able to learn to make good decisions in a complex, changing world.