@conference {1511433,
	title = { Eliminating expensive chunks by restricting expressiveness},
	booktitle = { International Joint Conference on Artificial Intelligence (IJCAI{\textquoteright}89) },
	year = {1989},
	abstract = {Chunking,    an    experience    based-learning    mechanism,    improves    Soar{\textquoteright}s    performance    a    great   deal    when   viewed    in    terms    of   the   number   of   subproblems  required  and  the  number  of  steps  within  a  subproblem.    This    high-level view  of  the  impact  of  chunking  on  performance  is  based  on  an  deal  computational  model,  which   says  that  the  time  per  step  is  constant.  However,   if   the   chunks   created   by   chunking   are   expensive,   then   they   consume   a   large   amount   of   processing   in   the   match,   i.e,   indexing   the   knowledge-base,  distorting  Soar*s  constant  time-per-stcp  model.     In  these  situations,  the  gain  in  number  of  steps  does  not  reflect  an  improvement  in  performance;  in  fact  there  may  be  degradation  in  the  total  run  time  of  the  system.   Such  chunks  form  a  major problem  for the  system,  since  absolutely  10 guarantees  can  be given about  its  behavior.  I   "his  article  presents  a  solution  to  the  problem  of  expensive  chunks.  The  solution  is  based  on  the  notion  of  restricting   the  expressiveness  of  Soar{\textquoteright}s  representational  language  to  guarantee  that  chunks  formed will  require  only  a  limited  amount  of  matching  effort.     We  analyze  the  tradeoffs  involved  in  restricting  expressiveness  and  present  some  empirical  evidence  to  support  our  analysis.},
	author = {M. Tambe and P. Rosenbloom}
}
