The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Reconstructive expert system explanation
Artificial Intelligence
Artificial Intelligence - Special volume on natural language processing
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Explaining reasoning in description logics
Explaining reasoning in description logics
Evaluating expert-authored rules for military reasoning
Proceedings of the 2nd international conference on Knowledge capture
Argos: dynamic composition of web services for goods movement analysis and planning
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Automatic generation of data processing workflows for transportation modeling
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Finding causes of program output with the Java Whyline
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Advanced Policy Explanations on the Web
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Extracting and answering why and why not questions about Java program output
ACM Transactions on Software Engineering and Methodology (TOSEM)
Datalog for security, privacy and trust
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
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When a query to a knowledge-based system fails and returns "unknown", users are confronted with a problem: Is relevant knowledge missing or incorrect? Is there a problem with the inference engine? Was the query ill-conceived? Finding the culprit in a large and complex knowledge base can be a hard and laborious task for knowledge engineers and might be impossible for non-expert users. To support such situations we developed a new tool called "WhyNot" as part of the PowerLoom knowledge representation and reasoning system. To debug a failed query, WhyNot tries to generate a small set of plausible partial proofs that can guide the user to what knowledge might have been missing, or where the system might have failed to make a relevant inference. A first version of the system has been deployed to help debug queries to a version of the Cyc knowledge base containing over 1,000,000 facts and over 35,000 rules.