  Many AI applications rely on knowledge about a relevant real-world domain
that is encoded by means of some logical knowledge base (KB). The most
essential benefit of logical KBs is the opportunity to perform automatic
reasoning to derive implicit knowledge or to answer complex queries about the
modeled domain. The feasibility of meaningful reasoning requires KBs to meet
some minimal quality criteria such as logical consistency. Without adequate
tool assistance, the task of resolving violated quality criteria in KBs can be
extremely tough even for domain experts, especially when the problematic KB
includes a large number of logical formulas or comprises complicated logical
formalisms.
  Published non-interactive debugging systems often cannot localize all
possible faults (incompleteness), suggest the deletion or modification of
unnecessarily large parts of the KB (non-minimality), return incorrect
solutions which lead to a repaired KB not satisfying the imposed quality
requirements (unsoundness) or suffer from poor scalability due to the inherent
complexity of the KB debugging problem. Even if a system is complete and sound
and considers only minimal solutions, there are generally exponentially many
solution candidates to select one from. However, any two repaired KBs obtained
from these candidates differ in their semantics in terms of entailments and
non-entailments. Selection of just any of these repaired KBs might result in
unexpected entailments, the loss of desired entailments or unwanted changes to
the KB.
  This work proposes complete, sound and optimal methods for the interactive
debugging of KBs that suggest the one (minimally invasive) error correction of
the faulty KB that yields a repaired KB with exactly the intended semantics.
Users, e.g. domain experts, are involved in the debugging process by answering
automatically generated queries about the intended domain.
