A framework for creating systems capable of adapting and dealing with norms based on software agents
Bibliographic record
Abstract
Abstract The software for distributed intelligent systems, also known as Multi-agent Systems (MASs), must possess the capability to comply with environmental constraints, such as norms, and exhibit self-adaptive mechanisms to autonomously modify their behavior in response to contextual changes and handle adverse situations independently. However, there is a lack of comprehensive understanding of the concepts of self-adaptation and normative systems, as well as their behavior and interactions, which is often not adequately supported by software tools. This research introduces an extension to the Framework for Normative Agent Java Simulation (JSAN), enabling implement normative adaptive agents based on an architecture capable of dealing with norms through self-adaptation. The extended framework provides support for the key properties, namely self-adaptation, norms, and normative reasoning. To validate the efficacy of this approach, a virtual marketplace study case is presented, showcasing the agents' ability to adapt to norms while facilitating user transactions and product purchases.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".