Artificial Intelligence
A semantical approach to nonmonotonic logics
Readings in nonmonotonic reasoning
A theory of diagnosis from first principles
Readings in nonmonotonic reasoning
Theoretical Computer Science - Thirteenth International Colloquim on Automata, Languages and Programming, Renne
Model-based reasoning: troubleshooting
Exploring artificial intelligence
The complexity of reasoning about knowledge and time. I. lower bounds
Journal of Computer and System Sciences - 18th Annual ACM Symposium on Theory of Computing (STOC), May 28-30, 1986
Knowledge, probability, and adversaries
Proceedings of the eighth annual ACM Symposium on Principles of distributed computing
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Using crude probability estimates to guide diagnosis
Artificial Intelligence
Probabilistic semantics for nonmonotonic reasoning: a survey
Proceedings of the first international conference on Principles of knowledge representation and reasoning
What does a conditional knowledge base entail?
Artificial Intelligence
A guide to completeness and complexity for modal logics of knowledge and belief
Artificial Intelligence
Generalized Kripke models for epistemic logic
TARK '92 Proceedings of the fourth conference on Theoretical aspects of reasoning about knowledge
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reasoning about Knowledge and Probability
Proceedings of the 2nd Conference on Theoretical Aspects of Reasoning about Knowledge
An axiomatic treatment of three qualitative decision criteria
Journal of the ACM (JACM)
Some contributions to nonmonotonic consequence
Journal of Computer Science and Technology
Bayesian Update of Recursive Agent Models
User Modeling and User-Adapted Interaction
The learning power of belief revision
TARK '98 Proceedings of the 7th conference on Theoretical aspects of rationality and knowledge
A framework for reasoning about rational agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Agents, beliefs, and plausible behavior in a temporal setting
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Reasoning about temporal properties of rational play
Annals of Mathematics and Artificial Intelligence
Rational play and rational beliefs under uncertainty
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Towards action prediction using a mental-level model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On the foundations of qualitative decision theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Situation calculus on a dense flow of time
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Prime forms and minimal change in propositional belief bases
Annals of Mathematics and Artificial Intelligence
A qualitative Markov assumption and its implications for belief change
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Studia Logica
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We propose a general framework in which to study belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes φ if he knows that φ is true in all the worlds he considers most plausible. We then consider some properties defining the interaction between knowledge and plausibility, and show how these properties affect the properties of belief. In particular, we show that by assuming two of the most natural properties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledge, plausibility (and hence belief), and time, which extends the framework of Halpern and Fagin [HF89] for modeling knowledge in multi-agent systems. We show that our framework is quite expressive and lets us model in a natural way a number of different scenarios for belief change. For example, we show how we can capture an analogue to prior probabilities, which can be updated by "conditioning". In a related paper, we show how the two best studied scenarios, belief revision and belief update, fit into the framework.