URL:http://www.cs.toronto.edu/~yilan Summary
Bibliographic record
Abstract
We design a representation based on the situation calculus that facilitates development, maintenance and elaboration of very large taxonomies of actions. It leads to a modular basic action theories (BATs) for reasoning about actions more compact than Reiter's current representation and consequently, more suitable for knowledge engineering in large realistic domains. We compare our representation with Reiter's BATs and prove that it inherits all useful properties of his BATs. In addition, it has significant computational advantages. For taxonomies of actions represented as finitely branching trees, the regression operator works exponentially faster for our theories than it works with Reiter's BATs. Research in Artificial Intelligence (AI) is concerned with producing machines to automate tasks requiring intelligent behaviour. Examples include planning, scheduling, and expert systems, etc. In order to achieve these tasks, one of the most important problems in AI is how to represent and reason about knowledge properly. In particular, my research interest lies in how to represent features of a dynamic environment properly and to reason about the effects of actions performed by auto-agents.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".