Web Service Composition as a Planning Task: Experiments using Knowledge-Based Planning
Why this work is in the frame
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Bibliographic record
Abstract
Motivated by the problem of automated Web service composition (WSC), in this paper, we present some empirical evidence to validate the effectiveness of using knowledge-based planning techniques for solving WSC problems. In our experiments we utilize the PKS (Planning with Knowledge and Sensing) planning system which is derived from a generalization of STRIPS. In PKS, the agent’s (incomplete) knowledge is represented by a set of databases and actions are modelled as revisions to the agent’s knowledge state rather than the state of the world. We argue that, despite the intrinsic limited expressiveness of this approach, typical WSC problems can be specified and solved at the knowledge level. We show that this approach scales relatively well under changing conditions (e.g. user constraints). Finally, we discuss implementation issues and propose some architectural guidelines within the context of an agent-oriented framework for inter-operable, intelligent, multi-agent systems for WSC and provisioning.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| 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 it