Building collaborative intelligent agents: revealing main pillars
Bibliographic record
Abstract
Software agents are persistent, knowledgeable, autonomous, collaborative, and learnable entities. One important feature of software agents is the ability to interact and communicate as a team to achieve more than they could individually. Although the belief-desire-intention (BDI) agent model is possibly the best known and used model of practical reasoning agent; nevertheless, this model does not address the key concept of how individual agent learn from the environment and manipulate itself to collaborate with others. In this paper we present a novel approach that customize the BDI model to define a so-called "RBDIA: Rapport-belief-desire-intention-adaptation" as a generic method to support progress from individual autonomous agent concept towards a collaborative multiple agents. Rapport here refers to the component that connects an agent to its environment, whereas adaptation module incorporates mechanisms of learning. We believe that the five proposed tiers for multiagent systems modeling serves for mastering the complexity and the difficulty of setting up effective autonomous collaborative MAS
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".