Integrating Acting, Planning and Learning in Hierarchical Operational Models

Autor:	Patra, Sunandita, Mason, James, Kumar, Amit, Ghallab, Malik, Traverso, Paolo, Nau, Dana
Rok vydání:	2020
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near-optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio. Comment: Accepted in ICAPS 2020 (30th International Conference on Automated Planning and Scheduling)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.03932 Zobrazit plný text záznamu View this record from Arxiv