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334 was based on one sample, less confidence should be placed on it that if based on ten-million samples. Belief maintenance systems need to be able to track and manipulate some measure of this uncertainty. The basis for this capability exists in belief networks. The conditional probabilities learned at the nodes are naturally represented as beta, or Dirichlet distributions. Musick (31) showed that those distributions can be correctly manipulated during inference, and returned in place of a point probability.
What is the chance that there is not a car coming, and how much should be risked betting on it? Another example: during the salary negotiation process in an interview, the interviewee will typically not know the true salary range the potential employer is willing to pay. 2. Outcomes of Actions. The result of an action may be uncertain or unknown. In the previous example, the agent may desire to reduce the uncertainty about the environment by doing a few observations. For example, the agent will both look right and left and listen for sounds of traffic.
ACM, 1986. 28. A. , A general lower bound on the number of examples needed for learning. In D. Haussler and L. ), Proc. 1988 Workshop on Computational Learning Theory, pages 139–154, Palo Alto, CA, Morgan Kaufmann, 1988. 29. E. B. Baum and D. Haussler, What size net gives valid generalization? In D. Z. ), Proc. Neural Information Processing Systems, pages 81–90, New York, American Inst. of Physics, 1988. 30. D. Haussler, Generalizing the PAC model: Sample size bounds from metric dimension-based uniform convergence results, in Proc.