We describe an algorithm, inspired by the biological behavior of ants, to efficiently determine intelligent moves and the genetic algorithm used to learn better strategies in a broad class of games. We present a class of games called Simple War Games that includes both deterministic and nondeterministic, perfect information, two-player, zero sum games. Simple War Games have a branching factor a...

Teaching computers to play video games is a complex learning problem that has recently seen increased attention. In this paper, we develop a system that, using constant model and hyperparameter settings, learns to play a variety of Atari games. In order to accomplish this task, we extract object features from the game screen, and provide these features as input into reinforcement learning algor...

Background: Despite recent UK Governmental policy changes concerning the amount of outdoor, physical activity children are currently engaged in (LTS, 2010), there is a gap in the academic literature concerning children’s personal preferences for structured play and learning through physical activity in outdoor environments. Aim and objectives: This research explores the context of residential o...

This paper discusses the implications of learning theory for the analysis of games with a move by Nature. One goal is to illuminate the issues that arise when modeling situations where players are learning about the distribution of Nature’s move as well as learning about the opponents’ strategies. A second goal is to argue that quite restrictive assumptions are necessary to justify the concept ...

In Hex two players try to connect opposing sides by placing pieces onto a rhombus-shaped board of hexagons. The game has a high strategic complexity and the number of possible board positions is larger than in Chess. There are already some Hex programs of recognizable strength, but which still play on a level below very strong human players. One of their major weaknesses is the time for evaluat...