Planning for millions of NPCs in Real-Time
Bibliographic record
Abstract
We address the problem of scaling the generation of plans in real-time to control the behaviors of several millions of Non-Player Characters (NPCs) in video-games and virtual worlds. Search-based action planning, introduced in the game F.E.A.R. in 2005, has an exponential time complexity managing at most several tens of NPCs per frame. A close study of the plans generated in first-person shooters shows that: (1) states are vectors of enumerated values, (2) both initial and final states can be totally defined, (3) actions are both post-unique and unary, (4) plans are totally ordered, and (5) actions occur only once in plans. (1) to (5) satisfy the Simplified Action Structure (SAS) linear time planning framework SAS-$\mathbb{T}_{1}$. We strengthen previous claims on this framework saying that the associated linear time algorithm$\mathbb{P}$is capable of managing several millions of NPCs per frame by testing it on three new realistic benchmarks that are based on commercial video games.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".