Abstract
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) |
|---|---|
| Pages (from-to) | 699-709 |
| Number of pages | 11 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 3220 |
| State | Published - Dec 1 2004 |
| Externally published | Yes |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)