Online Learning and Planning in Cognitive Hierarchies

• URL: http://arxiv.org/abs/2310.12386v1
• Date: Wed, 18 Oct 2023 23:53:51 GMT
• Title: Online Learning and Planning in Cognitive Hierarchies
• Authors: Bernhard Hengst, Maurice Pagnucco, David Rajaratnam, Claude Sammut, Michael Thielscher
• Abstract summary: We extend an existing formal framework to model complex integrated reasoning behaviours of robotic systems. New framework allows for a more flexible modelling of the interactions between different reasoning components.
• Score: 10.28577981317938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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