Probabilistic Logic Programming Semantics For Procedural Content Generation

Reference. Probabilistic Logic Programming Semantics For Procedural Content Generation

Research in procedural content generation (PCG) has recently heralded two major methodologies: machine learning (PCGML) and declarative programming. The former shows promise by automating the specification of quality criteria through latent patterns in data, while the latter offers significant advantages for authorial control. In this paper we propose the use of probabilistic logic as a unifying framework that combines the benefits of both methodologies. We propose a Bayesian formalization of content generators as probability distributions and show how common PCG tasks map naturally to operations on the distribution. Further, through a series of experiments with maze generation, we demonstrate how probabilistic logic semantics allows us to leverage the authorial control of declarative programming and the flexibility of learning from data.

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Cite as @madkour-2023-probabilistic (helia, typst) · \cite{madkour-2023-probabilistic} (LaTeX)

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bibtex · 1 line

@article{madkour-2023-probabilistic, title={Probabilistic Logic Programming Semantics For Procedural Content Generation}, volume={19}, ISSN={2326-909X}, url={http://dx.doi.org/10.1609/aiide.v19i1.27525}, DOI={10.1609/aiide.v19i1.27525}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, publisher={Association for the Advancement of Artificial Intelligence (AAAI)}, author={Madkour, Abdelrahman and Martens, Chris and Holtzen, Steven and Harteveld, Casper and Marsella, Stacy}, year={2023}, month=Oct, pages={295–305} }

hayagriva YAML (typst)

yaml · 21 lines

madkour-2023-probabilistic:
  type: article
  title: Probabilistic Logic Programming Semantics For Procedural Content Generation
  author:
  - Madkour, Abdelrahman
  - Martens, Chris
  - Holtzen, Steven
  - Harteveld, Casper
  - Marsella, Stacy
  date: 2023-10
  page-range: 295-305
  url: http://dx.doi.org/10.1609/aiide.v19i1.27525
  serial-number:
    doi: 10.1609/aiide.v19i1.27525
    issn: 2326-909X
  parent:
    type: periodical
    title: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
    publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    issue: 1
    volume: 19

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Cites 34 works (0 here)

External (34)

The Joy of Probabilistic Answer Set Programming: Semantics, Complexity, Expressivity, Inference (2020)
Generating Explorable Narrative Spaces with Answer Set Programming (2020)
Three Modern Roles for Logic in AI (2020)
Approximate Arithmetic Circuits: A Survey, Characterization, and Recent Applications (2020)
Efficient Generation of Structured Objects with Constrained Adversarial Networks (2020)
Deep Learning for Procedural Content Generation (2020)
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Taksim: A Constrained Graph Partitioning Framework for Procedural Content Generation (2019)
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Automatic Generation of Game Content Using a Graph-based Wave Function Collapse Algorithm (2019)
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