Propositional knowledge base revision and minimal change
Artificial Intelligence
On the complexity of propositional knowledge base revision, updates, and counterfactuals
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
An action language based on causal explanation: preliminary report
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
Knowledge, action, and the frame problem
Artificial Intelligence
Dynamic belief revision operators
Artificial Intelligence
Knowledge and the action description language 𝒜
Theory and Practice of Logic Programming
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Generalized update: belief change in dynamic settings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Iterated belief change in the situation calculus
Artificial Intelligence
Iterated belief change due to actions and observations
Journal of Artificial Intelligence Research
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Among the many and varied areas that Vladimir Lifschitz has worked on is reasoning about action and change, in particular with respect to action languages, where an action language in turn is based on the underlying semantic notion of a transition system. Transition systems have been shown to be an elegant, deceptively simple, yet rich framework from which to address problems of action consequence, causality, planning and the like. In this paper I consider a problem in the interaction between reasoning about action, observations, and the agent's knowledge, specifically when an observation conflicts with the agent's knowledge; and so the agent must revise its knowledge. In particular, it is shown how an agent's initial belief set may be propagated through an action sequence so that, in contrast to previous work, for a revision one does not need to refer back to the initial state of the agent.