A semantical approach to nonmonotonic logics
Readings in nonmonotonic reasoning
Chronological ignorance: experiments in nonmonotonic temporal reasoning
Artificial Intelligence
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
An introduction to possibilistic and fuzzy logics
Readings in uncertain reasoning
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Probabilistic semantics for nonmonotonic reasoning: a survey
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Propositional knowledge base revision and minimal change
Artificial Intelligence
The temporal logic of reactive and concurrent systems
The temporal logic of reactive and concurrent systems
Unifying default reasoning and belief revision in a modal framework
Artificial Intelligence
Conditional logics of normality: a modal approach
Artificial Intelligence
Conditional logics of belief change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Reasoning about knowledge
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
Abduction to plausible causes: an event-based model of belief update
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Modeling beliefs in dynamic systems
Modeling beliefs in dynamic systems
Modeling belief in dynamic systems, part I: foundations
Artificial Intelligence
A unified model of qualitative belief change: a dynamical systems perspective
Artificial Intelligence
Belief revision with unreliable observations
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fuzzy Measure Theory
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Revisions of knowledge systems using epistemic entrenchment
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
On the Use of an Extended Relational Model to Handle Changing Incomplete Information
IEEE Transactions on Software Engineering
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Plausibility measures: a user's guide
UAI'95 Proceedings of the Eleventh conference on Uncertainty in 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
Tableaux for Reasoning About Atomic Updates
LPAR '01 Proceedings of the Artificial Intelligence on Logic for Programming
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Filtering vs Revision and Update: Let Us Debate!
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Logic of Interaction for Multiagent Systems
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Remote Belief: Preserving Volition for Loosely Coupled Processes
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Heterogeneous temporal probabilistic agents
ACM Transactions on Computational Logic (TOCL)
Coordinating Self-interested Planning Agents
Autonomous Agents and Multi-Agent Systems
A Verified AsmL Implementation of Belief Revision
ABZ '08 Proceedings of the 1st international conference on Abstract State Machines, B and Z
Pr$\mathcal{SH}$: A Belief Description Logic
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Iterated Belief Revision in the Face of Uncertain Communication
Declarative Agent Languages and Technologies VI
An extended interpreted system model for epistemic logics
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Plausibility measures: a general approach for representing uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Reconstructing an agent's epistemic state from observations
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
PrDLs: a new kind of probabilistic description logics about belief
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Updating action domain descriptions
Artificial Intelligence
Belief extrapolation (or how to reason about observations and unpredicted change)
Artificial Intelligence
Automatic ontology evolution in open and dynamic computing environments
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
AGM belief revision in dynamic games
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.