Non-deterministic planning methods for automated web service composition
Bibliographic record
Abstract
Web service composition (WSC) is the task of generating new composite web services that exhibit functionalities not supported by any single web service. In its simplest form this is achieved by linking existing web services in sequence. More complex forms link services in parallel or use alternative paths. WSC can be considered a planning task, with the web services being the planning operators and the initial state and the goals being provided by the user. Particularly, since web services operate in a stochastic environment, their output is not predictable, and the problem is formulated as a non-deterministic planning one. This article presents a critical, comprehensive and up-to-date review of the literature concerning alternative non-deterministic planning methods, including probabilistic planning, determinization methods, planning in the belief state space and translation based methods. Furthermore, the article reviews existing implementations of WSC systems, employing a variety of planning approaches, and discusses the degree in which the current achievements from the non-deterministic planning field have been adopted successfully. To the best of our knowledge, this is the first review of its kind, one that provides a thorough introduction to the vast area of automated web service composition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".