We describe a theorem prover that is used in the Why2Atlas tutoring system for the purposes of evaluating the correctness of a student's essay and for guiding feedback to the student. The weighted abduction framework of the prover is augmented with various heuristics to assist in searching for a proof that maximizes measures of utility and plausibility. We focus on two new heuristics we added to the theorem prover: (a) a specificity-based cost for assuming an atom, and (b) a rule choice preference that is based on the similarity between the graph of cross-references between the propositions in a candidate rule and the graph of cross-references between the set of goals. The two heuristics are relevant to any abduction framework and knowledge representation that allow for a metric of specificity for a proposition and cross-referencing of propositions via shared variables.
|Original language||English (US)|
|Number of pages||11|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - Dec 1 2004|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)