Learning conceptual chess for testing evolutionary programming versus a reasoning-based soft expert system: The KASER
Bibliographic record
Abstract
A soft expert system is one that is qualitatively fuzzy. In this paper, we present such a system known as the ldquoknowledge amplification by structural expert randomizationrdquo system or KASER. This system facilitates reasoning using a domain specific expert and commonsense knowledge. It accomplishes this through object-classed predicates and an associated inference engine. The KASER addresses the high cost associated with the bottleneck of knowledge acquisition. Further, it also enables the entry of a basis of rules and provides for the automatic extension of that basis through domain symmetries. We will demonstrate the learning features of the KASER by comparing its capabilities with an evolutionary programming system that tries to learn the game of chess. In this paper, we concentrate on the evolutionary chess player and also describe the learning capabilities of the KASER, found through other tests. While this EP system may be able to play chess, the KASER provides knowledge as to why certain moves are employed as it learns the game. This powerful characteristic allows the KASER to learn supra-linearly, rather than through exhaustive searches. Thus, the KASER can be applied for many other scenarios in which learning through knowledge acquisition is employed.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".