TY - GEN
T1 - Learnability of term rewrite systems from positive examples
AU - Krishna Rao, M. R.K.
PY - 2006
Y1 - 2006
N2 - Learning from examples is an important characteristic feature of intelligence in both natural and artificial intelligent agents. In this paper, we study learnability of term rewriting systems from positive examples alone. We define a class of linear-bounded term rewriting systems that are inferable from positive examples. In linear-bounded term rewriting systems, nesting of defined symbols is allowed in right-hand sides, unlike the class of flat systems considered in Krishna Rao [8]. The class of linear-bounded TRSs is rich enough to include many divide-and-conquer programs like addition, logarithm, tree-count, list-count, split, append, reverse etc.
AB - Learning from examples is an important characteristic feature of intelligence in both natural and artificial intelligent agents. In this paper, we study learnability of term rewriting systems from positive examples alone. We define a class of linear-bounded term rewriting systems that are inferable from positive examples. In linear-bounded term rewriting systems, nesting of defined symbols is allowed in right-hand sides, unlike the class of flat systems considered in Krishna Rao [8]. The class of linear-bounded TRSs is rich enough to include many divide-and-conquer programs like addition, logarithm, tree-count, list-count, split, append, reverse etc.
UR - https://www.scopus.com/pages/publications/42649125416
M3 - Conference contribution
AN - SCOPUS:42649125416
SN - 1920682333
SN - 9781920682330
T3 - Conferences in Research and Practice in Information Technology Series
BT - Theory of Computing 2006 - Proceedings of the 12th Computing
ER -