認知階層におけるオンライン学習と計画
Online Learning and Planning in Cognitive Hierarchies
ロボットの複雑な推論行動を、記号的な計画から方策や遷移系のオンライン学習まで統一的に扱えるよう、既存の形式フレームワークを拡張した論文。
著者: Bernhard Hengst, Maurice Pagnucco, David Rajaratnam, Claude Sammut, Michael Thielscher
分類: cs.AI, cs.RO
原文アブストラクト
Complex robot behaviour typically requires the integration of multiple robotic and Artificial Intelligence (AI) techniques and components. Integrating such disparate components into a coherent system, while also ensuring global properties and behaviours, is a significant challenge for cognitive robotics. Using a formal framework to model the interactions between components can be an important step in dealing with this challenge. In this paper we extend an existing formal framework [Clark et al., 2016] to model complex integrated reasoning behaviours of robotic systems; from symbolic planning through to online learning of policies and transition systems. Furthermore the new framework allows for a more flexible modelling of the interactions between different reasoning components.
