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Entombed: An archaeological examination of an Atari 2600 game

2018· article· en· W2899691573 on OpenAlexafffund
John Aycock, Tara Copplestone

Bibliographic record

VenueThe Art Science and Engineering of Programming · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaStrong
KeywordsComputer scienceCode (set theory)ReuseTable (database)Video gameVariety (cybernetics)ExcavationArtificial intelligenceProgramming languageArchaeologyEngineeringMultimediaHistoryDatabase

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.023
GPT teacher head0.293
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2018
Admission routes2
Has abstractyes

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