Agent Skills
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aamas-experiments
GitHub用于AAMAS论文实验设计与审计，强调验证多智能体交互而非刷榜。涵盖对手选择、均衡/后悔指标、消融实验及统计报告规范，确保声明与证据匹配。
触发场景
设计AAMAS会议的多智能体实验
审计实验是否充分验证交互性声明
检查游戏理论与实证结果的对应关系
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-experiments -g -y
SKILL.md
Frontmatter
{
"name": "aamas-experiments",
"description": "Use when designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard."
}
AAMAS Experiments
Use this before submission when the empirical or simulation story is not yet locked. At AAMAS the experiment exists to test the interaction claim, not to top a benchmark.
Experiment audit
- Map each empirical claim to a game, a self-play run, a population sweep, an ablation, or a deviation test.
- Choose opponents deliberately: self-play alone rarely suffices; include held-out opponents, population sets, or classical strategies as the claim requires.
- Separate simulations that validate a solution concept (where the equilibrium is known) from real or applied studies that show practical multiagent behavior.
- Report uncertainty for stochastic results over both seeds and opponents: standard errors, confidence intervals, or paired tests.
- Report the environment, number of agents, training regime, evaluation protocol, metrics, hyperparameter ranges, chosen settings, seeds, hardware, software versions, and runtime.
- Add ablations for the interaction mechanism (communication, reward sharing, the payment rule), not just cosmetic variants.
- Audit for the mismatch between the strategic claim and the setup: an equilibrium claim tested against only one fixed opponent, or a cooperation claim that hides a reward-shaping constant.
What experiments are for at this venue
- The strongest design shows the interaction under stress: agents that can deviate, opponents the method did not train against, and populations that vary in size or composition.
- One experiment that lets agents try to exploit the mechanism and fails to profit is worth more than five extra environments where nothing strategic is tested.
- Reviewers, often game theorists, check whether the metric matches the claim: convergence to a named solution concept, exploitability, social welfare, or regret - not just episodic return.
Interaction-validation design table
| Interaction claim | Matching experiment | Reject pattern avoided |
|---|---|---|
| Converges to equilibrium | Convergence/exploitability curve under simultaneous adaptation | "Equilibrium asserted, never measured" |
| Mechanism is truthful | Strategic-deviation test: an agent tries to misreport | "Truthfulness proved, never stress-tested" |
| Beats other agents | Round-robin vs held-out opponents and a population | "Self-play only" |
| Emergent cooperation | Sweep over reward/opponent settings with variance | "One seed, one setting, one story" |
Vignette: a coordination-protocol study
Suppose the paper claims a learned protocol raises cooperation in a repeated public-goods game. The matching plan: sweep group size and defector fraction for cooperation curves, add held-out opponents that never appeared in training, and inject a free-rider agent to measure whether it profits - every panel tied to a numbered claim or definition.
Statistical reporting floor
- Seeds and replication counts for every stochastic curve; captions must state whether bands are standard errors, confidence intervals, or quantiles, and how many opponents were averaged.
- Report the compute actually consumed by self-play, not vague feasibility language.
Output format
[Experiment readiness] strong / adequate / weak
[Claim -> evidence map] <claim: game / self-play / population / deviation test>
[Missing interaction evidence] <opponents / deviation test / seeds / metric>
[Reproducibility gaps] <hyperparameters / compute / env / seeds>
[Decision-critical next run] <one experiment or simulation>
版本历史
- 9f86f09 当前 2026-07-19 14:15
