3 research outputs found
DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential Diagnosis
Given a system that does not work as expected, Sequential Diagnosis (SD) aims
at suggesting a series of system measurements to isolate the true explanation
for the system's misbehavior from a potentially exponential set of possible
explanations. To reason about the best next measurement, SD methods usually
require a sample of possible fault explanations at each step of the iterative
diagnostic process. The computation of this sample can be accomplished by
various diagnostic search algorithms. Among those, Reiter's HS-Tree is one of
the most popular due its desirable properties and general applicability.
Usually, HS-Tree is used in a stateless fashion throughout the SD process to
(re)compute a sample of possible fault explanations in each iteration, each
time given the latest (updated) system knowledge including all so-far collected
measurements. At this, the built search tree is discarded between two
iterations, although often large parts of the tree have to be rebuilt in the
next iteration, involving redundant operations and calls to costly reasoning
services.
As a remedy to this, we propose DynamicHS, a variant of HS-Tree that
maintains state throughout the diagnostic session and additionally embraces
special strategies to minimize the number of expensive reasoner invocations. In
this vein, DynamicHS provides an answer to a longstanding question posed by
Raymond Reiter in his seminal paper from 1987.
Extensive evaluations on real-world diagnosis problems prove the
reasonability of the DynamicHS and testify its clear superiority to HS-Tree
wrt. computation time. More specifically, DynamicHS outperformed HS-Tree in 96%
of the executed sequential diagnosis sessions and, per run, the latter required
up to 800% the time of the former. Remarkably, DynamicHS achieves these
performance improvements while preserving all desirable properties as well as
the general applicability of HS-Tree
Interactive Debugging of Knowledge Bases
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.Comment: Ph.D. Thesis, Alpen-Adria Universit\"at Klagenfur
Direct computation of diagnoses for ontology alignment β
Abstract. Modern ontology debugging methods allow efficient identification and localization of faulty axioms in an ontology. However, in many use cases such as ontology alignment the ontologies might include many conflict sets, i.e. sets of axioms preserving the faults, thus making ontology diagnosis infeasible. In this paper we present a debugging approach based on a direct computation of diagnoses that omits calculation of conflict sets. The evaluation results show that the approach is practicable and is able to identify a fault in adequate time. 1 Algorithm details and evaluation Most of the modern debugging approaches apply the model-based diagnosis [3] and compute diagnoses using conflict sets CS, i.e. irreducible sets of axioms ax i in an ontology O that preserve a fault. A user should modify at least all axioms of a diagnosis in order to be able to formulate the intended (target) ontology Ot. The computation of the conflict sets can be done within a polynomial number of calls to the reasoner, e.g. by QUICKXPLAIN algorithm [2]. To identify a diagnosis of cardinality |D | = m the hitting set algorithm suggested in [3] requires computation of m conflict sets. In the us