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aamas-reproducibility

Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.

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  • SKILL.md3.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

AAMAS Reproducibility

Use this before submission and again before camera-ready. The reproducibility question at AAMAS is not only "can I rerun the model" but "can I reproduce the interaction - the same agents, the same game, the same emergent outcome."

Evidence map

  • Map each theorem, mechanism property, convergence claim, and empirical interaction claim to a verifiable location in the paper, appendix, supplement, or artifact.
  • For theory, state the game, the information structure, the solution concept, assumptions, proof dependencies, and failure modes clearly enough for a game theorist.
  • For experiments, report the environment, number of agents, opponent/population set, training regime, evaluation opponents, metrics, hyperparameter ranges, chosen settings, seeds, repeated runs, compute, and runtime.
  • For small or noisy strategic differences, add uncertainty: standard errors, confidence intervals, or paired tests over seeds and over opponents.
  • Explain any missing code or environment honestly, and describe how a reader could reproduce the interaction in principle.
  • Keep the artifact consistent with the paper; a claim the artifact cannot demonstrate is a review-risk multiplier.

Claim-to-evidence audit table

ClaimPure-theory answerLearning-plus-game answer
Solution concept reachedProof with the game and information structure statedPlus convergence curves under other agents' adaptation
Opponents / populationNA if fully analyticalThe exact opponent set and how it was chosen
Seeds and varianceNA for deterministic resultsRequired for every stochastic curve and payoff table
ComputeNAHardware, per-run time, and total number of self-play runs

Claiming an equilibrium result while the evaluation only shows two fixed agents playing once is the recognizable AAMAS gap: reviewers read the mismatch between the strategic claim and the thinness of the interaction evidence as carelessness about the rest.

Vignette: a MARL-plus-convergence paper

Consider a submission proving convergence to a coarse-correlated equilibrium in a repeated game, validated by self-play. Its reproducibility spine: the game generator and payoff scale, the learning rule and its step sizes, the opponent set, the replication seeds, the convergence metric, and one honest sentence on the regime where convergence is only empirical, not proved.

Degrees of reproducibility

  • Turnkey: one command reruns the game and regenerates each convergence figure from logged seeds.
  • Scripted: scripts exist but require documented manual steps or an external environment.
  • Descriptive: prose detailed enough that a competent reader could rebuild the game and agents.

For AAMAS, the strategic core should be turnkey because reviewers actually re-run small games; large real-world or human-in-the-loop pipelines may stay scripted with deviations documented. Stating the achieved level honestly beats overpromising turnkey behavior that fails on a clean machine.

Output format

[Claim inventory] <claim -> evidence location>
[Artifact consistency] complete / inconsistent / missing
[Interaction reproducibility gaps] <game/opponents/seeds/uncertainty/compute>
[Paper fixes] <must appear in main PDF>
[Supplement fixes] <appendix or artifact additions>

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同一仓库的技能

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.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills13022026年9月27日 更新

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.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills13022026年9月27日 更新

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.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills13022026年9月27日 更新

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.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills13022026年9月27日 更新

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.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills13022026年9月27日 更新

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.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills13022026年9月27日 更新

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