AI in arbitrary world

TL;DR

This paper explores creating AI for the game Tic-Tac-Toe, focusing on modeling the world with automata and formulas to handle partial observability and complexity.

Contribution

It introduces a novel approach using automata and first-order formulas to model and understand an incomplete, uncertain environment in Tic-Tac-Toe.

Findings

The proposed model effectively captures the game's dynamics.

AI can make informed decisions despite partial information.

The approach demonstrates potential for more complex environments.

Abstract

In order to build AI we have to create a program which copes well in an arbitrary world. In this paper we will restrict our attention on one concrete world, which represents the game Tick-Tack-Toe. This world is a very simple one but it is sufficiently complicated for our task because most people cannot manage with it. The main difficulty in this world is that the player cannot see the entire internal state of the world so he has to build a model in order to understand the world. The model which we will offer will consist of final automata and first order formulas.