Author manuscript, published in "IEEE Conference on Computational Intelligence and Games, Niagara Falls: Canada (2013)" MirrorBot: Using Human-inspired Mirroring Behavior To Pass A Turing Test
Bibliographic record
Abstract
Abstract—Believability of automated characters in virtual worlds has posed a challenge for many years. In this paper, the author discusses a novel approach of using human-inspired mirroring behavior in MirrorBot, an Unreal Tournament 2004 game bot which crossed the humanness barrier and won the 2K BotPrize 2012 competition with the score of 52.2%, a record in the five year history of this contest. A comparison with past contest entries is presented and the relevance of the mirroring behavior as a humanness improvement factor is argued. The modules that compose MirrorBot’s architecture are presented along with a discussion of the advantages of this approach and proposed solutions for its drawbacks. The contribution continues with a discussion of the bot’s results in humanness and judging accuracy. I.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.375 | 0.108 |
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".