Framework for Human-Like Reasoning for Agents Operating in the Internet of Things

Integrating evidence from a single sensor over time is becoming more common due to the Internet of Things (IoT). In a typical embedded system a sensor is measuring a particular set of values over time. Raw sensor data is often integrated over time using the Kalman filter. Some sensors, however, output classifications and probabilities over time rather than values that generate sequences of beliefs. Integration of beliefs has tended to rely on approaches such as Bayes and Dempster-Shafer (D-S). While these methods are well founded for sensor fusion across multiple sensors, their adoption to integrating beliefs from single sources over time is not. There are many issues with using traditional probabilistic approaches, including: i) They are order independent ii) They are not designed for dynamic environments iii) They are not well-suited for assignable errors in the sensor measurements. All of these issues may cause significant evidential conflict and errors. We have been developing an alternate approach for sequential evidence accumulation that integrates the set theoretic nature of Dempster-Shafer theory with Kalman-based estimation. This approach is motivated by traditional signal processing and human psychology. These models were shown effective and this paper will show how to implement forgetting which can be useful in dynamic settings when sensor drop-outs occur.