Ecai Experiments

ECAI Experiments

The ECAI question is always the same: is the evidence proportional to the claim? But ECAI spans symbolic and applied AI, so "evidence" ranges from a proof to a fair empirical comparison. Choosing the right kind of evidence for your claim shape is the first and most important decision.

Choose the evidence type by claim shape

A provable claim needs a proof; an empirical claim needs a fair, seeded comparison; a claim about understanding needs an explanation, not just a number.

For theory / KR / planning / argumentation

• Prove it, completely. The body sketches; the supplement carries full proofs (ecai-reproducibility). State every assumption (finiteness, admissibility, language fragment).
• Standard instances for empirical planning/search. Use recognized domains/benchmarks (e.g. the community's standard planning domains) so node/quality numbers are comparable; report per-domain results, not just an aggregate.
• Complexity claims get the reduction or the algorithm, not a hand-wave.

For ML / learning-based contributions

• Fair baselines, fairly tuned. Give the baseline the same tuning budget as your method; a hobbled baseline is the fastest way to lose a reviewer.
• Seeds and spread. Report mean and variance/CI across multiple seeds; a single run is not evidence. State the number of runs.
• Explain the win. ECAI rewards why a method works (an ablation isolating the responsible component, a theoretical reason) over a leaderboard delta.
• Contamination and leakage. For LLM/pretrained components, check train/test overlap and document model identifiers with dates; cache outputs so results reproduce.

For multi-agent contributions

• Specify the environment, agents, episodes, and metrics exactly; multi-agent results are notoriously protocol-sensitive.
• Compare against the right baselines for the setting (cooperative/competitive), and report across seeds and environment variations, not one map.
• If the contribution is fundamentally about agent interaction, sanity-check whether AAMAS is the better-matched pool (ecai-topic-selection).

For applied AI (PAIS)

• Lead with the real-world claim and constraints (data availability, latency, cost, safety), not a benchmark score.
• Show the method survives real conditions; a deployment story that only reports offline accuracy under-delivers on the PAIS bar.

Ablations and honesty

• Ablate the mechanism you credit. If you attribute the gain to component C, remove C and show the drop.
• Report negative and null results where they bound the claim — in a single-round process, self-reported limits cost less than reviewer-discovered ones (ecai-review-process).
• No cherry-picking domains, seeds, or metrics; report the protocol that generated every number.

Fit the 7-page body

Evidence a reviewer needs to judge the claim (the proof idea, the key comparison, the main table) stays in the body; full proofs, extra domains, and ablation grids go to the supplement (ecai-supplementary). Do not exile the decision-critical comparison to save space.

Output format

[Claim -> evidence] each claim mapped to proof / controlled comparison / deployment demo
[Proof completeness] provable claims proved with explicit assumptions? yes/no
[Baseline fairness] baselines tuned comparably? seeds + spread reported?
[Why it works] mechanism explained (ablation/theory), not just a number? yes/no
[Provenance] datasets/models/seeds pinned; outputs cached? gaps: <list>
[Body/supplement] decision-critical evidence inside 7 pages? yes/no