Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway
Bibliographic record
Abstract
For a better understanding of the nature of complex systems modeling, computer simulations and the analysis of the resulting data are major tools which can be applied. In this paper, we study a statistical modeling problem of data coming from a simulation model that investigates the correctness of autonomous agents’ decisions in learning to cross a cellular automaton-based highway. The goal is a better understanding of cognitive agents’ performance in learning to cross a cellular automaton-based highway with different traffic density. We investigate the effects of parameters’ values of the simulation model (e.g., knowledge base transfer, car creation probability, agents’ fear and desire to cross the highway) and their interactions on cognitive agents’ decisions (i.e., correct crossing decisions, incorrect crossing decisions, correct waiting decisions, and incorrect waiting decisions). We firstly utilize canonical correlation analysis (CCA) to see if all the considered parameters’ values and decision types are significantly statistically correlated, so that no considered dependent variables or independent variables (i.e., decision types and configuration parameters, respectively) can be omitted from the simulation model in potential future studies. After CCA, we then use the regression tree method to explore the effects of model configuration parameters’ values on the agents’ decisions. In particular, we focus on the discussion of the effects of the knowledge base transfer, which is a key factor in the investigation on how accumulated knowledge/information about the agents’ performance in one traffic environment affects the agents’ learning outcomes in another traffic environment. This factor affects the cognitive agents’ decision-making abilities in a major way in a new traffic environment where the cognitive agents start learning from existing accumulated knowledge/information about their performance in an environment with different traffic density. The obtained results provide us with a better understanding of how cognitive agents learn to cross the highway, i.e., how the knowledge base transfer as a factor affects the experimental outcomes. Furthermore, the proposed methodology can become useful in modeling and analyzing data coming from other computer simulation models and can provide an approach for better understanding a factor or treatment effect.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".