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<record>
  <title>Implementation of Top-Direct Abductive Learning based on Mapping ILP Approach</title>
  <journal>Journal of Data Processing</journal>
  <author>Domenico Corapi, Alessandra Russo and Emil Lupu</author>
  <volume>14</volume>
  <issue>1</issue>
  <year>2024</year>
  <doi>https://doi.org/10.6025/jdp/2024/14/1/11-20</doi>
  <url>https://www.dline.info/jdp/fulltext/v14n1/jdpv14n1_2.pdf</url>
  <abstract>It is introducing a new non-monotonic ILP ( NILP ) approach, and its implementation is called top-direct abductive learning (TAL). TAL addresses some shortcomings of inverse-entailed ILP systems and is the first Top-Down ILP system to allow background theories and hypotheses to be normal logic programs. Talâ€™s approach is based on mapping an ILP problem to an equivalent ALP one. This allows the use of well-established ALP proof procedures and the specification of richer language biases with integrity constraints. The mapping provides a principled search space for the ILP problem and allows an abductive search to compute inductive solutions.</abstract>
</record>
