Cross-Domain Action-Model Acquisition for Planning via Web Search

Applying learning techniques to acquire action models is an area of intense research interest. Most previous works in this area have assumed that there is a significant amount of training data available in a planning domain of interest, which we call target domain, where action models are to be learned. However, it is often difficult to acquire sufficient training data to ensure that the learned action models are of high quality. In this paper, we develop a novel approach to learning action models with limited training data in the target domain by transferring knowledge from related auxiliary or source domains. We assume that the action models in the source domains have already been created before, and seek to transfer as much of the the available information from the source domains as possible to help our learning task. We first exploit a Web searching method to bridge the target and source domains, such that transferrable knowledge from source domains is identified. We then enc...

• Type: Journal
• Year: 2011
• Where: AIPS
• Authors: Hankz Hankui Zhuo, Qiang Yang, Rong Pan, Lei Li
• Keywords: Action Models, AIPS 2011, Artificial Intelligence, Intense Research, Target Domain