Propositional knowledge base revision and minimal change
Artificial Intelligence
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
A unified model of qualitative belief change: a dynamical systems perspective
Artificial Intelligence
Quatum logic, Hilbert space, revision theory
Artificial Intelligence
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Probabilistic belief change: expansion, conditioning and constraining
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On a linear framework for belief dynamics in multi-agent environments
CLIMA VII'06 Proceedings of the 7th international conference on Computational logic in multi-agent systems
On a linear framework for belief dynamics in multi-agent environments
CLIMA VII'06 Proceedings of the 7th international conference on Computational logic in multi-agent systems
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In this paper, we present a method to deal with the quantitative belief change based on the linear algebra. Since an epistemic state of agent is represented by a set of the subjective probability, which she conceived for each possible world, we can regard this epistemic state as a point of vector space which spanned by the basis of possible worlds. The knowledge which causes the belief change is treated as a matrix on this vector space. The observation of new fact about the current world is characterized as a projection matrix. On the other hand, the knowledge that some action changes the world is represented as a basis transformation matrix. In this framework, we present a unified method of belief change both for propositional and probabilistic knowledge so that the logical or probabilistic reasoning is reduced to the matrix calculation.