Although many investigators affirm a desire to build reasoning systems that behave consistently with the axiomatic basis defined by probability theory and uttlity 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 the problems that can be handled effectively to cases where it is clear that there is insufficient computational or engineering resource to perform an analysis deemed to be 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 -bow knowledge about the structure of user utility can be used to control value uadeoffs for tailoring inference to alternative contexts. We finally 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.
Addressing this problem, Horvitz REF introduces flexible computation methodologies, where knowledge of model utility is used to control the trade offs between additional computations and acting with partial results.
Reasoning About Beliefs and Actions Under Computational Resource Constraints
ArXiv
ArXiv
Computer Science Mathematics