Abstract
Proceedings of the 21st Conference on Artificial Intelligence (AAAI-06). Boston, MA, July 16â€"20, 2006. We consider the iterated belief change that occurs following an alternating sequence of actions and observations. At each instant, an agent has some beliefs about the action that occurs as well as beliefs about the resulting state of the world. We represent such problems by a sequence of ranking functions, so an agent assigns a quantitative plausibility value to every action and every state at each point in time. The resulting formalism is able to represent fallible knowledge, erroneous perception, exogenous actions, and failed actions. We illustrate that our framework is a generalization of several existing approaches to belief change, and it appropriately captures the non-elementary interaction between belief update and belief revision.,Conference paper,Published.