Goal models as run-time entities in context-aware systems
Bibliographic record
Abstract
The strength of goal models is their ability to assess candidate solutions against high level criteria for many stakeholders, allowing system-wide trade-offs to be performed. We argue that, in a context-aware system, reasoning based on goal models can complement standard rule-based reasoning engines for decision making without involving explicit interaction with the user. While rule-based systems excel in filtering out unsuitable solutions based on clear criteria, it is difficult to rank suitable solutions based on vague, qualitative criteria of stakeholders with a rule-based approach. The User Requirements Notation (URN) is a goal-based and scenario-based requirements modeling language that has been applied to many different domains, from reactive systems to telecommunication standards to business processes. For context-aware systems, URN's workflow notation can describe the overall behavior of a context-aware system and URN's goal models can further enhance reasoning about contextual situations. While URN already supports some of the interactions between workflow and goal models required for the specification of context-aware systems, it does not yet fully support the modeling, design-time simulation, and run-time execution of a context-aware system based on its URN model. This paper (i) introduces such a modeling, simulation, and execution environment, (ii) discusses three architectural solutions for combined rule-based and goal-oriented reasoning, and (iii) reports on a URN profile that describes a domain-specific language for context-aware reasoning using goal-orientation with the help of an example application from the health care domain.
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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".