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Learning the Rules of the Game : An Interpretable AI for Learning How to Play

In this article, we present an interpretable artificial intelligence, and its associated machine learning algorithm, that is capable of automatically learning the rules of a game whenever the rules—the relationship between a player’s current state and their corresponding set of legal moves—can be represented as a set of low degree Zhegalkin polynomials, a special class of Boolean functions. This is true for many popular games including Spanish Dominó and the card game President . Our method takes advantage of such low polynomial degree to compute an exact representation of the rules in polynomial time instead of the required exponential time for generic Boolean functions. The rules can also be represented using significantly less storage than in the generic case which, for many games, leads to a representation that is easy to interpret.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON GAMES; Vol.14 No.2 June 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 253-261

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BOOLEAN FUNCTIONS
GAME RULES
INTERPRETABLE ARTIFICIAL INTELLIGENCE
ZHEGALKIN POLYNOMIALS

Info Detail Spesifik

DOI: 10.1109/TG.2021.3066245

Pernyataan Tanggungjawab

Jared L. Aurentz, Adrian Martinez Navarro, David Rios Insua

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