Bibliographic record
Abstract
Nowadays, multiagent systems became a widely used technology in everyday life. More studies are needed to evaluate these systems from different aspects such as evaluating agent dialogues, the participants to these dialogues, and the protocols governing the dialogues, etc. In this paper, we define new measures for dialogue games from an external agent's point of view. In particular, two measurement sets are proposed: in the first set, we use Shannon entropy to measure the certainty index of the dialogue. This involves i) using Shannon entropy to measure the agent's certainty about each move during the dialogue; and ii) using Shannon entropy to measure the certainty of the agents about the whole dialogue with two different ways. The first way is by taking the average of the certainty index of all moves, and the second way is by determining all possible dialogues and applying the general formula of Shannon entropy. In the second set, we introduce two metrics: i) measuring the goodness of the agents in the real dialogue (i.e. the dialogue that effectively happened between the participants); and ii) measuring the farness of the agents from the right dialogue (i.e. the best dialogue that can be produced by two agents if they know the knowledge bases of each other). Many dialogue game types have been proposed in multiagent systems. In this paper, we focus on one specific type, namely quantitative negotiation such as bargaining.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".