Particle-Based Communication Among Game Agents
Bibliographic record
Abstract
One approach to creating realistic game AI is to create autonomous agents that can perform effectively with no more knowledge than a human player would have in their place. In a multi-agent setting, it is also necessary to devise a means for communicating among agents in collaborative game scenarios (such as a group of controlled agents that are searching for the player), since agents no longer have access to global knowledge. We present a method for communication using particle filters in the setting of game state estimation. Particle filters are an efficient, nonparametric means of performing inference in complex environments. Their use in game AI is particularly compelling, as they provide an easy way to represent nonlinear, non-Gaussian inferences about the state space, while exhibiting computational thrift. We demonstrate that communication among a group of agents — using particle filters to reason about the state space — can be accomplished in a natural way by sharing particles among the agents' filters. We also show how a criterion for deciding when to communicate naturally falls out of this framework. We apply this model in the setting of coordinated target detection, and find that agents of heterogenous types and complexities can nevertheless coordinate effectively
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".