Modeling and Reasoning with Decision-Theoretic Goals
Bibliographic record
Abstract
<p>Goal models have found important applications in Requirements Engineering as models that relate stakeholder requirements with system or human tasks needed to fulfill them. Often, such task specifications constitute rather idealized plans for requirements fulfillment, where task execution always succeeds. In reality, however, there is always uncertainty as to whether a specification can/will actually be executed as planned. In this paper, we introduce the concept of decision-theoretic goals in order to represent and reason about both uncertainty and preferential utility in goal models. Thus, goal models are extended to express probabilistic effects of actions and also capture the utility of each effect with respect to stakeholder priorities. Further, using a state-of-the-art reasoning tool, analysts can find optimal courses of actions/plans for fulfilling stakeholder goals while investigating the risks of those plans. The technique is applied in a real world meeting scheduling problem, as well as the London Ambulance Service case study.</p>
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".