  Although many investigators affirm a desire to build reasoning systems that
behave consistently with the axiomatic basis defined by probability theory and
utility theory, limited resources for engineering and computation can make a
complete normative analysis impossible. We attempt to move discussion beyond
the debate over the scope of problems that can be handled effectively to cases
where it is clear that there are insufficient computational resources to
perform an analysis deemed as complete. Under these conditions, we stress the
importance of considering the expected costs and benefits of applying
alternative approximation procedures and heuristics for computation and
knowledge acquisition. We discuss how knowledge about the structure of user
utility can be used to control value tradeoffs for tailoring inference to
alternative contexts. We address the notion of real-time rationality, focusing
on the application of knowledge about the expected timewise-refinement
abilities of reasoning strategies to balance the benefits of additional
computation with the costs of acting with a partial result. We discuss the
benefits of applying decision theory to control the solution of difficult
problems given limitations and uncertainty in reasoning resources.
