An Explanation Oriented Dialogue Approach for Solving Wicked Planning Problems.
Bibliographic record
Abstract
In this paper we discuss support for solving complex “wicked ” planning problems by dialogues and explanations. Wicked problems are essentially imprecisely formulated problems, i.e. those that do not have a clear goal, well defined methods, and are subject to personal opinions of involved stakeholders that may be changeable. The method contains the following steps: (1) Reducing the complexity of the problem by the selecting a specific concern; (2) Obtaining a user defined ideal plan, called a prototype; (3) Comparing the actually generated plan and the prototype by a similarity measure. This will be aided by an explanation oriented dialogue. A major problem for the explanation is that we need to explain the result of an optimization procedure which excludes classical parsing oriented methods. The approach is generic and was instantiated in release planning, investment, and urban planning. We have simplified the original problems significantly in order to illustrate the principal approach. 1.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".