Special Issue on Logical Aspects of Multi-Agent Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| 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 it