AAMAS Research Topic Selection
Evaluates multiagent systems research projects to determine publication fit for AAMAS versus other top-tier AI and ML venues.
The AAMAS Topic Selection skill assists researchers in assessing whether their multiagent systems and AI projects align with the core focus of the AAMAS conference or are better suited for venues like NeurIPS, ICML, AAAI, IJCAI, EC, or the JAAMAS journal. It helps identify critical interaction primitives such as solution concepts, mechanisms, negotiation protocols, and coordination guarantees, while running diagnostic checks like the 'frozen-agent test' to ensure the multiagent aspect is central to the research rather than merely decorative.
주요 기능
01Diagnostic evaluation using the 'frozen-agent test' to prevent single-agent masking rejections
02Identification and framing of interaction primitives such as game-theoretic equilibria and coordination mechanisms
031,069 GitHub stars
04Fit signal analysis to distinguish genuine multiagent contributions from single-agent benchmarks
05Standardized output formatting covering venue recommendations, rejection risks, and actionable next steps
06Venue comparison mapping across AAMAS, NeurIPS, ICML, AAAI, IJCAI, EC, and JAAMAS
사용 사례
01Sharpening the multiagent framing and interaction primitive before drafting a conference paper
02Deciding whether a multiagent reinforcement learning project belongs at AAMAS, NeurIPS, or ICML
03Identifying top rejection risks related to scope and evaluation methodology during early-stage research