Learning and representation of event-discrete situations for individualized situation recognition using fuzzy Situation-Operator Modeling

Abstract

Knowledge representation and the ability to learn new knowledge define the success of situation recognition within cognitive systems. In this contribution, it is assumed that a complex environment in which a system is acting is modeled using an event-discrete approach. The modeling would be based on imprecise and uncertain knowledge about the environment, which is adapted by suitable learning abilities during the interaction between the system and environment.The main contribution of this paper is developing an approximate reasoning approach driven by learning and reusing human-operator experiences to handle event-discrete situations in unknown dynamic environments. A new approach for modeling and learning the knowledge for situation recognition in human operator assistance systems is proposed. Situation recognition is individualized for humans by learning exclusive experiences of human operators in interaction with the environment. Individualization is caused by variety of human operators in definition of priorities and goals, generation of events, and different environmental characteristics importance in specification of situations. For modeling interaction-based knowledge structures, a fuzzy Situation-Operator Modeling approach is used and improved by applying a feature selection process. The corresponding approach is able to learn and represent new knowledge to improve the performance of individualized situation recognition for cognitive systems.Here, the proposed approach is applied using a simulated driving environment and evaluated for different test drivers. Evaluation results highlight the ability and importance of the proposed approach for situation recognition in driving assistance systems.