• DocumentCode
    1101482
  • Title

    (P, p) Retraining Policies

  • Author

    Katsikopoulos, Konstantinos V.

  • Author_Institution
    Max Planck Inst. for Human Dev., Berlin
  • Volume
    37
  • Issue
    5
  • fYear
    2007
  • Firstpage
    609
  • Lastpage
    613
  • Abstract
    Skills that are practiced infrequently need to be retrained. A retraining policy is optimal if it minimizes the cost of keeping the probability that the skill is learned within two bounds. The (P, p) policy is to retrain only when the probability that the skill is learned has dropped just above the lower bound, so that this probability is brought up just below the upper bound. For minimum assumptions on the cost function, a set of two easy-to-check conditions involving the relearning and forgetting functions guarantees the optimality of the (P, p) policy. The conditions hold for power functions proposed in the psychology of learning and forgetting but not for exponential functions.
  • Keywords
    training; cost function; exponential functions; forgetting functions; lower bound; probability; relearning functions; retraining policy; upper bound; Cost function; Dynamic programming; Earthquakes; Floods; Humans; Inventory management; Memory management; Psychology; Testing; Upper bound; Dynamic programming; instruction; inventory management; memory; optimality;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4427
  • Type

    jour

  • DOI
    10.1109/TSMCA.2007.902620
  • Filename
    4292223