Entombed: An archaeological examination of an Atari 2600 game
Bibliographic record
Abstract
The act and experience of programming is, at its heart, a fundamentally human activity that results in the production of artifacts.When considering programming, therefore, it would be a glaring omission to not involve people who specialize in studying artifacts and the human activity that yields them: archaeologists.Here we consider this with respect to computer games.We draw from the nascent archaeological subarea of archaeogaming to carry out a digital excavation of the code and techniques used in the implementation of Entombed, an Atari 2600 game released in 1982 by US Games.The player in this game is, appropriately, an archaeologist who must make their way through a zombie-infested maze.Maze generation is a fruitful area for comparative retrogame archaeology, because a number of early games on different platforms featured mazes, and their variety of approaches can be compared.The maze in Entombed is particularly interesting: it is shaped in part by the extensive real-time constraints of the Atari 2600 platform, and also had to be generated efficiently and use next to no memory.We reverse engineered key areas of the game's code to uncover its unusual maze-generation algorithm, which we have also built a reconstruction of, and analyzed the mysterious table that drives it.In addition, we discovered what appears to be a 35-year-old bug in the code, as well as direct evidence of code-reuse practices amongst game developers.What further makes this game's development interesting is that, in an era where video games were typically solo projects, a total of five people were involved in various ways with Entombed.We piece together some of the backstory of the game's development and intoxicant-fueled design using interviews to complement our technical work.Finally, we contextualize this example in archaeology and lay the groundwork for a broader interdisciplinary discussion about programming, one that includes both computer scientists and archaeologists.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".