Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
日本語の概要は準備中です。原文の説明を表示しています。
Use when positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the cross-community citation coverage AAMAS reviewers expect.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission, anonymity, and prior-publication rules before advising authors; much of the nearest multiagent work lives at other venues, which shapes both positioning and eligibility.
| Literature lane | Typical sources | What AAMAS reviewers check |
|---|---|---|
| Agents / multiagent core | AAMAS, JAAMAS, IJCAI, AAAI agent tracks | Whether the nearest multiagent method is compared or explicitly distinguished |
| Multiagent RL | NeurIPS, ICML, ICLR, prior AAMAS | Whether MADDPG/QMIX/COMA-style cousins are cited even though they are not AAMAS papers |
| Game theory / mechanism design | EC, GEB, algorithmic game-theory venues | Whether the solution concept and known impossibility results are acknowledged |
| Social choice / negotiation | AAMAS, AAAI, COMSOC, ADT | Whether standard axioms and strategyproofness usage are followed |
A bibliography citing only deep-MARL benchmarks tells a game-theory reviewer that known equilibrium or mechanism results may be getting rediscovered - a recognizable AAMAS reject pattern no amount of benchmark strength repairs. The mirror failure - only citing classical game theory while ignoring the deep-MARL wave - loses the empirical reviewer.
Imagine the paper proposes a communication protocol that improves cooperation in a mixed-motive game. Its nearest neighbors: a NeurIPS MARL paper with emergent communication but no incentive analysis, an EC paper with a cheap-talk equilibrium but no learning, and a prior AAMAS paper on the same game with a weaker coordination result. The novelty sentence should name all three: incentives where the MARL line had none, learning where the game-theory line stayed static, and stronger coordination than the direct AAMAS predecessor.
[Eligibility] clear / needs declaration / risky
[Closest literatures] <agents / MARL / game-theory / social-choice>
[Nearest 3 works] <work -> distinction>
[Archival-overlap risk] <none / issues>
[Novelty sentence] <AAMAS-ready interaction contrast>
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
日本語の概要は準備中です。原文の説明を表示しています。
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
日本語の概要は準備中です。原文の説明を表示しています。
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
日本語の概要は準備中です。原文の説明を表示しています。
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
日本語の概要は準備中です。原文の説明を表示しています。
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
日本語の概要は準備中です。原文の説明を表示しています。
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
日本語の概要は準備中です。原文の説明を表示しています。