Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Bibliographic record
Abstract
The Autonomous Agents and Multi-agent Systems (AAMAS) conference series brings together researchers from around the world to share the latest advances in the field. It was initiated in 2002 as a merger of three highly successful related events: the International Conference on Autonomous Agents (AGENTS), the International Conference on Multi-Agent Systems (ICMAS), and the International Workshop on Agent Theories, Architectures, and Languages (ATAL). The AAMAS conference series provides a marquee, high-profile forum for research in the theory and practice of autonomous agents and multi-agent systems. AAMAS 2002, the first of the series, was held in Bologna, followed by AAMAS 2003 in Melbourne, AAMAS 2004 in New York, AAMAS 2005 in Utrecht, AAMAS 2006 in Hakodate, AAMAS 2007 in Honolulu, AAMAS 2008 in Estoril and AAMAS 2009 in Budapest. You are now about to enter the proceedings of AAMAS 2010 as held in Toronto, Canada. In addition to the general track for the AAMAS 2010 conference, submissions were invited to two special tracks: a robotics track and a track on virtual agents The aims of these special tracks was to give researchers from these areas a strong focus and to provide a forum for discussion and debate within the encompassing structure of AAMAS, which we hoped to guarantee by appointing leaders in the field as track chairs (Michael Beetz for the robotics track, Stacy Marsella for the virtual agents track). The special track chairs managed both the reviewer allocation and the review process itself with their own chosen PC members: decisions concerning acceptance of papers were taken in discussion and in full agreement with the AAMAS 2010 Program Co-chairs. Only full paper submissions were solicited for AAMAS 2010. The general, robotics, and virtual agents tracks received 570, 57, and 58 submissions respectively, for a total of 685 submissions, a total that is slightly higher compared to the previous AAMAS. After a thorough and exciting review process, eventually 163 papers were selected for publication as full papers each of which was allocated 8 pages in the proceedings. Another 136 papers were selected as Extended Abstracts and allocated 2 pages each in the proceedings. Both full papers and extended abstracts are presented as posters during the conference.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.150 | 0.076 |
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".