Difficulties in forcing fairness of polynomial time inductive inference

  • Authors:

  • John Case;Timo Kötzing

  • Affiliations:

  • Department of Computer and Information Sciences, University of Delaware, Newark, DE;Department of Computer and Information Sciences, University of Delaware, Newark, DE

  • Venue:
  • ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
  • Year:
  • 2009

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Abstract

There are difficulties obtaining fair feasibility from polynomial time updated language learning in the limit from positive data. Pitt 1989 noted that unfair delaying tricks can achieve polynomial time updates but with no feasibility constraint on the whole learning process. In this context Yoshinaka 2009 makes a useful list of properties or restrictions towards true feasibility. He also provides interesting examples of fair polynomial time algorithms featuring particular uniformly polynomial time decidable hypothesis spaces, and each of his algorithms satisfies several of his properties. Yoshinaka claims that the combination of the three restrictions on polynomial time learners of consistency (which we call herein postdictive completeness), conservativeness and prudence is restrictive enough to stop Pitt's delaying tricks from working. The present paper refutes the claim of the previous paragraph in three settings. In the setting of uniformly polynomial time decidable hypothesis spaces with a few effective closure properties, the three restrictions allow maximal unfairness. The other two settings involve certain other uniformly decidable hypothesis spaces and general language learning hypothesis spaces. In each of these settings, the three restrictions forbid some, but not all Pitt-style delaying tricks. Inside the proofs of each of our theorems asserting that the three restrictions do not forbid some or all delaying tricks, the witnessing learners can be seen to explicitly employ delaying tricks.