An architectural framework for hybrid intelligent systems: Implementation issues
Bibliographic record
Abstract
This paper presents an implemented framework for intelligent system integration based on the concept of intercommunicating hybrids. The implemented toolset based on the framework is called the Intelligent Forecasters Construction Set (IFCS), which is a hybrid-programming environment that allows the developer to implement forecasters by means of neural network modules, object-oriented visual programming, knowledge-based programming and procedural programming. Neural network modules, rules, procedures and other intelligent techniques are encapsulated into blocks which can connect with each other as data flow diagrams for data processing. The flow diagrams can be organized into a hierarchy of workspaces to solve problems. The system was implemented on the real-time expert system shell G211G2, GDA and NeurOn-Line (NOL) are trademarks of Gensym Corp., USA., with G2 Diagnostic Assistant (GDA1) and NeurOn-Line1 (NOL) modules. The modularity of IFCS allows subsequent addition of other modules of intelligent techniques. The IFCS was used for developing forecasters of daily electricity demand and water demand at the City of Regina based on the idea of homogeneous multi-module system. In both cases, the data sets were separated into subclasses and each of them was modeled with a neural network module. The two problem domains were also modeled using a linear regression (LR) and a case based reasoning (CBR) program. The benefits of a multi-module neural network approach are discussed and some experimental results from the applications are presented.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".