Reports of the 2016 AAAI Workshop Program
Bibliographic record
Abstract
The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirtieth AAAI Conference on Artificial Intelligence (AAAI‐16) was held at the beginning of the conference, February 12–13, 2016. Workshop participants met and discussed issues with a selected focus, and the workshop provided an informal setting for active exchange among researchers, developers, and users on topics of current interest. The AAAI‐16 workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The 15 workshops held at AAAI‐16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS‐16‐01), AI, Ethics, and Society (WS‐16‐02), Artificial Intelligence for Cyber Security (WS‐16‐03), Artificial Intelligence for Smart Grids and Smart Buildings (WS‐16‐04), Beyond NP (WS‐16‐05), Computer Poker and Imperfect Information Games (WS‐16‐06), Declarative Learning Based Programming (WS‐16‐07), Expanding the Boundaries of Health Informatics Using AI (WS‐16‐08), Incentives and Trust in Electronic Communities (WS‐16‐09), Knowledge Extraction from Text (WS‐16‐10), Multiagent Interaction Without Prior Coordination (WS‐16‐11), Planning for Hybrid Systems (WS‐16‐12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS‐16‐13), Symbiotic Cognitive Systems (WS‐16‐14), and World Wide Web and Population Health Intelligence (WS‐16‐15).
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.158 | 0.126 |
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".