Robot Ludens: Inducing the Semblance of Life in Machines
Bibliographic record
Abstract
As culture and technology move forward, researchers from various fields have continuously developed new ways to create lifelike machines that evoke empathy and engagement with humans. This shared goal drives an enormous effort in the realms of arts, games, and social machines, among others. The present study's core research problem revolves around understanding how non-living devices gain a semblance of life and intelligence. The goal is to identify and examine the expressive components that induce the attribution of life into machines. The research follows the methodological framework of research-creation, integrating theory with creative processes. The theoretical framework's key findings establish the following four design elements for creating lifelike machines: Body, Behavior, Context, and Name. It examines how the articulation of those elements can assign different meanings to devices. In association with the theoretical framework, this study also involves producing two examples of lifelike machines within the realms of art and game design. The first is a robotic art installation called Robot Ludens, exploring possibilities and challenges for creating robots playing alive and playing dead. The other is a drawing game called Pict.io, which tries to simulate camaraderie dynamics between humans and computational players.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".