能動学習における高次認知モデルの役割
The Role of Higher-Order Cognitive Models in Active Learning
人間を能動的なデータ源として扱い、人間とAIが互いの意図を多段に推論する高次認知モデルを組み込んだ能動学習の枠組みを提唱し、その計算的検証を行った論文。
著者: Oskar Keurulainen, Gokhan Alcan, Ville Kyrki
分類: cs.LG, cs.RO
原文アブストラクト
Building machines capable of efficiently collaborating with humans has been a longstanding goal in artificial intelligence. Especially in the presence of uncertainties, optimal cooperation often requires that humans and artificial agents model each other's behavior and use these models to infer underlying goals, beliefs or intentions, potentially involving multiple levels of recursion. Empirical evidence for such higher-order cognition in human behavior is also provided by previous works in cognitive science, linguistics, and robotics. We advocate for a new paradigm for active learning for human feedback that utilises humans as active data sources while accounting for their higher levels of agency. In particular, we discuss how increasing level of agency results in qualitatively different forms of rational communication between an active learning system and a teacher. Additionally, we provide a practical example of active learning using a higher-order cognitive model. This is accompanied by a computational study that underscores the unique behaviors that this model produces.
