# Inductive general game playing

arXiv: https://arxiv.org/abs/1906.09627 · HTML: https://mlsanity.com/paper/1906.09627/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2019.

## Built on by (most cited first, 18 of 18)

- [Inductive logic programming at 30](https://mlsanity.com/paper/2102.10556/) (2021)
- [Learning MDL logic programs from noisy data](https://mlsanity.com/paper/2308.09393/) (2023)
- [Learning logic programs by combining programs](https://mlsanity.com/paper/2206.01614/) (2022)
- [Learning Logic Programs From Noisy Failures](https://mlsanity.com/paper/2201.03702/) (2021)
- [From Code to Play: Benchmarking Program Search for Games Using Large Language Models](https://mlsanity.com/paper/2412.04057/) (2024)
- [Learning logic programs by discovering where not to search](https://mlsanity.com/paper/2202.09806/) (2022)
- [Learning big logical rules by joining small rules](https://mlsanity.com/paper/2401.16215/) (2024)
- [Automatic Generation of Board Game Manuals](https://mlsanity.com/paper/2109.09507/) (2021)
- [Learning programs with magic values](https://mlsanity.com/paper/2208.03238/) (2022)
- [Learning logic programs through divide, constrain, and conquer](https://mlsanity.com/paper/2109.07818/) (2021)
- [Learning logic programs by explaining their failures](https://mlsanity.com/paper/2102.12551/) (2021)
- [Parallel Constraint-Driven Inductive Logic Programming](https://mlsanity.com/paper/2109.07132/) (2021)
- [Efficient rule induction by ignoring pointless rules](https://mlsanity.com/paper/2502.01232/) (2025)
- [An Empirical Comparison of Cost Functions in Inductive Logic Programming](https://mlsanity.com/paper/2503.07554/) (2025)
- [Honey, I shrunk the hypothesis space (through logical preprocessing)](https://mlsanity.com/paper/2506.06739/) (2025)
- [Symmetry breaking for inductive logic programming](https://mlsanity.com/paper/2508.06263/) (2025)
- [Symbolic Snapshot Ensembles](https://mlsanity.com/paper/2510.24633/) (2025)
- [Continual learning and refinement of causal models through dynamic predicate invention](https://mlsanity.com/paper/2602.17217/) (2026)

## Abstract (arXiv)

General game playing (GGP) is a framework for evaluating an agent's general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all the games. In this paper, we invert this task: a learner is given game traces and the task is to learn the rules that could produce the traces. This problem is central to inductive general game playing (IGGP). We introduce a technique that automatically generates IGGP tasks from GGP games. We introduce an IGGP dataset which contains traces from 50 diverse games, such as Sudoku, Sokoban, and Checkers. We claim that IGGP is difficult for existing inductive logic programming (ILP) approaches. To support this claim, we evaluate existing ILP systems on our dataset. Our empirical results show that most of the games cannot be correctly learned by existing systems. The best performing system solves only 40% of the tasks perfectly. Our results suggest that IGGP poses many challenges to existing approaches. Furthermore, because we can automatically generate IGGP tasks from GGP games, our dataset will continue to grow with the GGP competition, as new games are added every year. We therefore think that the IGGP problem and dataset will be valuable for motivating and evaluating future research.