Learning Behavioral Memory Representations from Observation

Learning from Observation (LfO) is highly useful for modeling behaviors through nonintrusive observation of some actor's performance. However, an actor's performance is often influenced by unobservable internal influences, such as emotions, agendas, and memory of past events. Therefore, new techniques are needed to infer the structure of these influences and their effect on an actor's decisions. In this paper, we propose a novel approach called Memory Composition Learning (MCL) for capturing one internal influence: memory of past events. We hypothesize that memory influences on a behavior can be modeled through parameterized memory features that can be learned from observation of traces of an actor's behavior; these memory features can then be presented as additional input to a performance modeling application. We demonstrate the efficacy of our approach in a simulated vacuum cleaner domain and show that hidden memory influences can be detected, modeled, and then used to improve machine learning performance.