Special Issue on Logical Aspects of Multi-Agent Systems
Bibliographic record
Abstract
There is a growing interdisciplinary community of researchers and research groups working on logical aspects of MAS from the perspectives of logic, artificial intelligence, computer science, game theory, etc.The workshop Logical Aspects of Multi-Agent Systems (LAMAS) serves the community as a platform for presentation, exchange, and publication of ideas.The idea for and the name of LAMAS actually emerged independently at two locations: in 2002 and 2007, Hans van Ditmarsch organised two editions of LAMAS in Dunedin, New Zealand.From 2010, the community around the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) saw the need for a workshop on logical aspects of multi-agent systems, and the workshop LAMAS has since been held in Toronto, Canada (2010), Osuna, Spain (2011), Valencia, Spain (2012), Toulouse, France (2013), and Paris, France (2014), with the editions in even years being colocated with AAMAS.In this special issues we present the post-proceedings of the 7th LAMAS, together with extended versions of selected papers of the 15th AAMAS, which were both held in Paris (2014).We invited authors of selected papers to submit extended versions of their papers to this special issue.All submissions had to pass a fresh selection process according to the standards of Studia Logica.We believe that the two selection processes resulted in a collection of papers of high quality.The contributions address different facets of logical aspects of multi-agent systems, including the decidability of multi-agent logics, model checking, logics for agent/robot reasoning and game-theoretical aspects.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.022 |
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".