Project 2 Using Prolog as a Knowledge-Based System CSE 4/563, Knowledge Representation
Bibliographic record
Abstract
This project is adapted from Russell & Norvig’s AI texts [1,2]. You are to specify and formalize a domain sufficient to answer a series of questions about the information in the sentence (which I will call sentence S): Yesterday, John went to the Wegmans Alberta Drive supermarket and bought a pound of tomatoes and two pounds of ground beef. You are then to load the knowledge base (KB) and your formalization of S into SICStus Prolog [3], and have it answer the following questions. Each question is followed by the correct answer in square brackets. 1. Is John a child or an adult? [Adult] 2. Does John now have at least two tomatoes? [Yes] 3. Did John buy any meat? [Yes] 4. If Mary was buying tomatoes at the same time as John, did he see her? [Yes] 5. Are the tomatoes made in the supermarket? [No] 6. What is John going to do with the tomatoes? [Eat them] 7. Does Wegmans sell deodorant? [Yes] 8. Did John bring any money to the supermarket? [Yes] 9. Does John have less money after going to the supermarket? [Yes] [1,2] 2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".