Decisions, Uncertainty, and Computation
The Decision, Uncertainty, and Computation area seeks
submissions describing the most critical developments in the realm of
decision, inference, and action under uncertainty.
Topics of interest include
developments in graphical models for inference of probabilistic and
decision-theoretic inference, exact and approximate inference
algorithms, learning models from data, computational models of time,
persistence, and causation, Markov processes and planning under
uncertainty, abstraction and qualitative inference,
qualititative decision theory, and decision
making under scarce or uncertain resource limitations.
We also invite contributions on key results on uncertainty
and utility in the control, synthesis, and evaluation of computational
processes, including, but not limited to, procedures for inferring
belief and action.