Browse Skills

Helps researchers decide whether a project is a strong fit for AAMAS or better suited to other AI venues like AAAI, ICML, or EC. Use before writing to sharpen multiagent framing and avoid desk rejection.

Guides structuring of AAMAS supplementary material including proofs, extended definitions, additional experiments, and artifact zips while ensuring anonymity and adherence to reviewer guidelines.

Audit an AAMAS paper submission for compliance with formatting, anonymity, track selection, and other requirements to avoid desk rejection. Useful for researchers preparing to submit to the AAMAS conference.

Guides authors through the AAMAS peer review process, from initial submission on OpenReview to rebuttal and final decision. Helps identify leverage points and avoid common pitfalls in the double-blind review cycle.

Use when preparing AAMAS submissions to strengthen reproducibility evidence for interaction claims: map claims to evidence, check agent consistency, document game definitions, opponent sets, seeds, and uncertainty.

Audit novelty and eligibility for AAMAS submissions by positioning against multiagent literature across communities, checking cross-community coverage, and managing concurrent works.

Design and audit experiments for AAMAS papers, ensuring multi-agent interaction claims are rigorously tested through proper opponent selection, game-theoretic simulations, and statistical validation.

Helps authors convert accepted AAMAS papers into camera-ready format, handling de-anonymization, two-column reflow, rebuttal promise integration, metadata, and artifact release. Use after acceptance.

Drafts responses to preliminary AAMAS reviews, handling game-theory, MARL, and systems reviewer feedback under double-blind and no-new-results constraints to help area chairs reach acceptance.

Package multiagent code, environments, opponent sets, and logs as anonymous supplementary evidence for AAMAS peer review, enabling game-theory and MARL reviewers to inspect and re-run interaction claims.

Use when revising an AAAI manuscript for broad-AI-audience fit, a first-page contribution statement legible to non-specialist Phase-1 reviewers, two-column readability, concise novelty claims, reproducibility-checklist alignment, hedged limitations and ethics, and policy-aware framing of AI-system and capability claims.

Use when planning an AAAI project timeline from topic selection through abstract, OpenReview submission, two-phase review, rebuttal, decision, camera-ready, registration, presentation, and public artifact release.