Bibliographic record
Abstract
With the evolution of software engineering since the advent of structured programming until now, software engineers are faced with tremendous challenges mostly due to the development of large software programs that behave as open systems. Multi-agent systems, which consist of multiple cooperating intelligent agents within an environment, form a particular class of such systems. This sort of software program, which carries out operations by repeatedly interacting with dynamic environments, has become more and more complex with the emergence of ubiquitous communication and computing technologies that constantly grow and evolve. Agent-oriented computing constitutes an appealing solution for coping with this level of complexity because systems can be built by combining agents. The automated composition of software artifacts to generate new ones may rest on recent progress in artificial intelligence and automatic control that has its roots in the tradition of program synthesis. The goal is to provide software engineers with effective methods in which the planning and control of software actions are integral parts of composition operators, together with synthesis procedures that automatically generate an execution strategy that governs the discrete dynamics of the composed artifact in order to satisfy given requirements. This approach ensures a higher degree of safety because it relies on formal methods. This paper shows how the behavior composition problem issued from the artificial intelligence community can be solved within the framework of the supervisory control theory with the aim to benefit from all of its rich facets.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".