Giovanni
Casini and Umberto Straccia. Belief change
based on knowledge measures.
In Journal of Logic and
Computation, Volume 36(2), Oxford University Press, 2026. Doi
Abstract:
Belief Change (BC) is the process of changing beliefs (in our
case, in terms of contraction, expansion and revision) taking into
account a new piece of knowledge, which possibly may be in
contradiction with the current belief.
In this work, we present a novel quantitative BC framework based on
the principle of minimizing the surprise from an
information-theoretic perspective. Central to our approach is the Principle
of Minimal Surprise (PMS), which asserts that when confronted
with the uncertainty about which is the actual world, an agent
should tend to favour the most expected, i.e., least surprising,
outcomes. To formalize this, we make use of Knowledge
Measures (KMs), which quantify the amount of information
contained in a knowledge base. Guided by the PMS, our framework
encourages belief change operations that minimize the informational
amount deviation from the original belief, i.e., those that
introduce the least surprise.