Context Inference Including Cause Reasoning and Prediction

Abstract

It is not enough to recognize the situations which currently occur simply. The current situations have the causes that they get to occur. The causes can just be generated and they can make the situations like the current states while the ones which occurred in the past have continued. Furthermore, if the causes which made the current situations don't disappear, they can continue to stay the same, get worse, or be changed to another situation. Therefore, limiting the range of context awareness to the situations which currently occur can be insufficient as the system which recognizes situations of the everyday world. Therefore, this study aims at problem-solving of two things. First, it recognizes situations without advance information. Second, it infers causes of situations and predicts how the situations will turn out in the future. To solve these problems, this study uses multiple sensor data fusion together using Dempster-Shafer Evidence Theory (DST) and Kalman Filter (KF). It recognizes situations under the conditions without any advance information through DST, infers causes of the current situations, and predicts how the current situations will turn out in the future. At this moment, BPA is important to recognize situations through DST and infer causes and state transition equation plays an important role in predicting arrangement through KF. The study carries out context inference and cause inference using DST. It describes the plan which infers causes of situations without advance information. It calculates required state transition equation to predict the progress of the research and infers how the causes revealed through DST by using it will arrange the current situations in the future by using KF.