In this paper, we present a semantical approach to multi-agent belief revision and belief update. For this, we introduce relational structures called conditional doxastic models (...
We describe a simple variant of the interpolated Markov model with nonemitting state transitions and prove that it is strictly more powerful than any Markov model. More importantl...
We study conditional computational entropy: the amount of randomness a distribution appears to have to a computationally bounded observer who is given some correlated information....
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
Using variational analysis techniques, we study convex-composite optimization problems. In connection with such a problem, we introduce several new notions as variances of the clas...