Interpreting Tangled Program Graphs Under Partially Observable Dota 2 Invoker Tasks
Bibliographic record
Abstract
Interpretable machine learning (ML) implies that structural relationships to exist between different parts of the ML model. We demonstrate how the tangled program graph (TPG) framework is able to demonstrate structural relationships for a suite of partially observable reinforcement learning tasks. The tasks are defined in terms of developing spell casting behaviours for the Invoker hero under the Dota 2 game engine. We show that TPG is able to demonstrate all 4 of the properties used to define interpretable machine learning. Moreover, a unique form of feature engineering / modularity takes place between programs that define state for (indexed) memory versus programs defining actions. The full article appears as Smith and Heywood (2024) "Interpreting Tangled Program Graphs under Partially Observable Dota 2 Invoker tasks" in IEEE Transactions on Artificial Intelligence, 5 (4): 1511–1524. https://doi.org/10.1109/TAI.2023.3279057
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".