Hybrid Learning Approach Toward Situation Recognition and Handling

Abstract

We propose a novel hybrid learning approach to gain situation awareness in smart environments by introducing a new situation identifier that combines an expert system and a machine learning approach. Traditionally, expert systems and machine learning approaches have been widely used independently to detect ongoing situations as the main functionality in smart environments in various domains. Expert systems lack the functionality to adapt the system to each user and are expensive to design based on each setting. On the other hand, machine learning approaches fail in the challenge of cold start and making explainable decisions. Using both of these approaches enables the system to use user's feedback and capture environmental changes while exploiting the initial expert knowledge to solve the mentioned challenges. We use decision trees and situation templates as the core structure to interpret sensor data. To evaluate the proposed method, we generate a new human-annotated dataset simulating a smart environment. Our experiments show superior results compared with the initial expert system and the machine learning approach while preserving the initial expert system's interpretability.