Search (1 results, page 1 of 1)

  • × author_ss:"Korf, R.E."
  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[1990 TO 2000}
  1. Zhang, W.; Korf, R.E.: Performance of linear-space search algorithms (1995) 0.00 
    6.9697306E-4 = product of:
  0.010454595 = sum of:
    0.0076375115 = weight(_text_:in in 4744) [ClassicSimilarity], result of:
      0.0076375115 = score(doc=4744,freq=6.0), product of:
        0.029340398 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.021569785 = queryNorm
        0.260307 = fieldWeight in 4744, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.078125 = fieldNorm(doc=4744)
    0.0028170836 = weight(_text_:s in 4744) [ClassicSimilarity], result of:
      0.0028170836 = score(doc=4744,freq=2.0), product of:
        0.023451481 = queryWeight, product of:
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.021569785 = queryNorm
        0.120123915 = fieldWeight in 4744, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.0872376 = idf(docFreq=40523, maxDocs=44218)
          0.078125 = fieldNorm(doc=4744)
  0.06666667 = coord(2/30)
    Abstract
    Search algorithms in artificial intelligence systems that use space linear in the search depth are employed in practice to solve difficult problems optimally, such as planning and scheduling. Studies the average-case performance of linear-space search algorithms, including depth-first branch-and-bound, iterative-deepening, and recursive best-first search
    Source
    Artificial intelligence. 79(1995) no.2, S.241-292