An Interaction Protocol for Mutual Assistance in Agent Teamwork
Bibliographic record
Abstract
This paper proposes and explores an interaction protocol for incorporating helpful behavior into agent teamwork. In the proposed Mutual Assistance Protocol (MAP), an agent can directly assist a teammate who requests help, provided that the two agents jointly determine, based on their individual beliefs, that the expected outcome of the help act is in the interest of the team. This distributed decision is reached through a bidding sequence similar to the one in the Contract Net Protocol. The deliberation about help is approximate in that each agent only assesses the team impact of the change to its own individual plan. The paper introduces two versions of the protocol: Action MAP, in which the helper performs an action within a teammate's individual plan, and Resource MAP, in which one or more helpers provide resources to a teammate. Both versions include refinements for the handling of simultaneous help requests. A cooperative game simulation demonstrates the advantages of Action MAP over action help protocols that use unilateral decision mechanisms, and over teamwork scenarios without help. The experiments show how the team performance depends on: the teammates' mutual awareness of each other's abilities, dynamic disturbance in the environment, communication costs, and computation costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".