  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.
