ACLP: Integrating Abduction and Constraint Solving
  ACLP is a system which combines abductive reasoning and constraint solving by
integrating the frameworks of Abductive Logic Programming (ALP) and Constraint
Logic Programming (CLP). It forms a general high-level knowledge representation
environment for abductive problems in Artificial Intelligence and other areas.
In ACLP, the task of abduction is supported and enhanced by its non-trivial
integration with constraint solving facilitating its application to complex
problems. The ACLP system is currently implemented on top of the CLP language
of ECLiPSe as a meta-interpreter exploiting its underlying constraint solver
for finite domains. It has been applied to the problems of planning and
scheduling in order to test its computational effectiveness compared with the
direct use of the (lower level) constraint solving framework of CLP on which it
is built. These experiments provide evidence that the abductive framework of
ACLP does not compromise significantly the computational efficiency of the
solutions. Other experiments show the natural ability of ACLP to accommodate
easily and in a robust way new or changing requirements of the original
problem.
