Belief change with noisy sensing in the situation calculus
  Situation calculus has been applied widely in artificial intelligence to
model and reason about actions and changes in dynamic systems. Since actions
carried out by agents will cause constant changes of the agents' beliefs, how
to manage these changes is a very important issue. Shapiro et al. [22] is one
of the studies that considered this issue. However, in this framework, the
problem of noisy sensing, which often presents in real-world applications, is
not considered. As a consequence, noisy sensing actions in this framework will
lead to an agent facing inconsistent situation and subsequently the agent
cannot proceed further. In this paper, we investigate how noisy sensing actions
can be handled in iterated belief change within the situation calculus
formalism. We extend the framework proposed in [22] with the capability of
managing noisy sensings. We demonstrate that an agent can still detect the
actual situation when the ratio of noisy sensing actions vs. accurate sensing
actions is limited. We prove that our framework subsumes the iterated belief
change strategy in [22] when all sensing actions are accurate. Furthermore, we
prove that our framework can adequately handle belief introspection, mistaken
beliefs, belief revision and belief update even with noisy sensing, as done in
[22] with accurate sensing actions only.
