A Comparison of Case-Based Reasoning and Probabilistic Graphical Models in the Context of Learning from Observation
Bibliographic record
Abstract
Learning from observation is a technique whereby learning occurs through observation or experience.In this work, we compare two existing techniques of learning from observation: Probabilistic Graphical Models (PGM) and Case-Based Reasoning (CBR) with the goal of identifying a preferred approach for future improvement.We show that the Naive Bayes Classifier is better than a previously used PGM model in learning behavior in a vacuum cleaner domain and introduce a state-based retrieval technique in CBR and show that there is no once-size-fits-all approach to learn state-based behavior.We also compare the two learning techniques in fully and partially observable continuous domains, namely Cartpole V0, obstacle avoidance, and 2D RoboCup.We show that the CBR approach works best in Cartpole V0, the PGM approach works best in obstacle avoidance, and the PGM approach works best in 2D RoboCup.Ultimately, we show that the preferred technique is generally behavior and domain specific.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".