A Comparison of Case Acquisition Strategies for Learning from Observations of State-Based Experts

This paper focuses on case acquisition strategies in the context of Case-based Learning from Observation (CBLfO). In Learning from Observation (LfO), a system learns behaviors by observing an expert rather than being explicitly programmed. Specifically, we focus on the problem of learning behaviors from experts that reason using internal state information, that is, information that can not be directly observed. The unobservability of this state information means that the behaviors can not be represented by a simple perception-to-action mapping. We propose a new case acquisition strategy called "Similarity-based Chunking", and compare it with existing strategies to address this problem. Additionally, since standard classification accuracy in predicting the expert's actions is known to be a poor measure for evaluating LfO systems, we propose a new evaluation procedure based on two complementary metrics: behavior performance and similarity with the expert.