How do you revise your belief set with %$;@*?
This work addresses a foundational issue in belief revision for AI and cognitive science, proposing a novel extension to account for dynamic perception and latent acceptance.
The paper tackles the problem of static belief representation in classic AGM belief revision theory by arguing that agents accept both visible and latent parts of information, with latent beliefs potentially becoming visible as beliefs change. It presents a perception-based belief theory that incorporates latent beliefs.
In the classic AGM belief revision theory, beliefs are static and do not change their own shape. For instance, if p is accepted by a rational agent, it will remain p to the agent. But such rarely happens to us. Often, when we accept some information p, what is actually accepted is not the whole p, but only a portion of it; not necessarily because we select the portion but because p must be perceived. Only the perceived p is accepted; and the perception is subject to what we already believe (know). What may, however, happen to the rest of p that initially escaped our attention? In this work we argue that the invisible part is also accepted to the agent, if only unconsciously. Hence some parts of p are accepted as visible, while some other parts as latent, beliefs. The division is not static. As the set of beliefs changes, what were hidden may become visible. We present a perception-based belief theory that incorporates latent beliefs.