Context preference-based deep adaptive resonance theory: Integrating user preferences into episodic memory encoding and retrieval

Dick Sigmund, Gyeong-Moon Park, Jong-Hwan Kim

Abstract: Episodic memory which can store and recall episodes has been modeled by various research. Those models focus on encoding and retrieving the same sequence of events of episodes. In this paper, we propose context preference-based deep adaptive resonance theory (CPD-ART). CPD-ART uses a new approach in encoding and retrieving a temporal sequence of events considering subjects, preference criteria such as weather, and object contexts such as beverage. A new layer, context preference field, is added to the encoding and retrieval processes for decision making. Context preference field encodes and stores the knowledge of criteria and object contexts, along with their relations in probability weight vectors. Simulation results demonstrate that CPD-ART is able to conduct decision making analysis and retrieve the sequence of events of an episode correctly through decision making analysis based on subjects, preference criteria, and the object contexts.