Linear Temporal Public Announcement Logic: a new perspective for reasoning about the knowledge of multi-classifiers
This work addresses formal reasoning about knowledge in multi-classifier systems, but it appears incremental as it combines existing logics without new empirical results.
The paper tackles the problem of formally modeling knowledge extraction in classification processes by proposing LTPAL, a transition system combining Public Announcement Logic and Linear Temporal Logic, and demonstrates it with a video-stream object detection example.
In this note, a formal transition system model called LTPAL to extract knowledge in a classification process is suggested. The model combines the Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL). In the model, first, we consider classifiers, which capture single-framed data. Next, we took classifiers for data-stream data input into consideration. Finally, we formalize natural language properties in LTPAL with a video-stream object detection sample.