Using behaviour patterns to generate scripts for computer role-playing games
Why this work is in the frame
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Bibliographic record
Abstract
Character behaviours in computer role-playing games have a significant impact on game-play, but are often difficult for story authors to implement and modify. Many computer games use custom scripts to control the behaviours of non-player characters (NPCs). Therefore, a story author must write fragments of computer code for the hundreds or thousands of NPCs in the game world. The challenge is to create non-repetitive (more entertaining) behaviours for the NPCs without investing substantial programming effort to write custom non-trivial scripts for each NPC. Consequently, current computer games mostly rely on simplistic non-interactive behaviours for NPCs. This research describes the design and implementation of a novel behaviour model for interacting NPCs, based on generative design patterns, that requires no manual script writing. In this model, NPCs assume different roles during the story and select behaviours based on static probabilities or dynamic motivations. We also devised a reinforcement learning algorithm, ALeRT, based on Sarsa(lambda) and we extended our behaviour model to support behaviour selection based on learning. In our model, an NPC can exhibit proactive, reactive, or latent behaviours that may be independent or collaborative. This behaviour architecture supports behaviours that can be interrupted and resumed based on priorities. The implementation of this model produces scripting code for BioWare Corp.'s Neverwinter Nights computer role-playing game.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it