Learning from Observation involves creating agents that observe experts performing tasks and imitate them. Case-Based Reasoning (CBR) is a tool that can be used for this purpose. Regular CBR can only learn memoryless behavior: behavior that doesn't rely on the past. Temporal Backtracking (TB) is an approach to learning state-based behavior that uses recency as its inductive bias, which may or may not be relevant to the agent behavior. We show how TB can be viewed as a particular case of a more generalized case-based approach to learning state-based behavior that can accommodate other inductive biases. We then propose five alternative similarity metrics to learn three different state-based behaviors in a 2D vacuum cleaner domain, and compare their performance to the TB algorithm's performance. We show that none of the proposed metrics (nor TB) is a one-size-fits all algorithm for learning state-based behavior.

The Cognitive Adaptive Learning Classification and Response for Communications Threats system, (CALCR) uses a case-based reasoning (CBR) and case-based learning (CBL) approach to address issues encountered in a contested RF communications environment. CALCR was the result of a research project that explored new approaches to understanding communications threats and responding with appropriate countermeasures. Modern communications threats may be modified from existing systems, or may be completely new systems, and CALCR enables a response to these unknown or unanticipated threats. CALCR integrates existing properties of CBR, along with several innovations, making it ideal for this problem: the ability for a case library to be extended through CBL as new conditions are encountered; the robustness of CBR in situations where there is missing data, which CALCR addresses with an advanced intelligent similarity measure; the ability to detect classes unknown to the case library through the use of a confidence measure; and the ability to provide a best-attempt solution, when multiple threat classes are matched, through the use of a new approach called the taxonomy reasoner.