Adds Quixo

This commit is contained in:
Nemo 2020-07-17 03:07:49 +05:30
parent 71b3e2e6ae
commit 083eb7fe41

View File

@ -28,6 +28,7 @@ games like Chess, Go, Hex, take a look at the [Chess programming wiki](https://w
- [Lost Cities](#lost-cities)
- [Uno](#uno)
- [Dominion](#dominion-1)
- [Quixo](#quixo)
- [Mobile Games](#mobile-games)
- [2048](#2048)
- [Game Design](#game-design)
@ -156,6 +157,10 @@ games like Chess, Go, Hex, take a look at the [Chess programming wiki](https://w
I couldn't find any published research on Dominion, but there is a [simulator](https://dominionsimulator.wordpress.com/f-a-q/) and the code behind
[the Dominion server running councilroom.com](https://github.com/mikemccllstr/dominionstats/) is available. There are [best and worst openings](http://councilroom.com/openings), [optimal card ratios](http://councilroom.com/optimal_card_ratios), [Card winning stats](http://councilroom.com/supply_win) and lots of other interesting stuff.
# Quixo
- [QUIXO is EXPTIME-complete](https://doi.org/10.1016/j.ipl.2020.105995)
# Mobile Games
- [Trainyard is NP-Hard](https://arxiv.org/abs/1603.00928)
- [Threes!, Fives, 1024!, and 2048 are Hard](https://arxiv.org/abs/1505.04274)
@ -176,5 +181,4 @@ I couldn't find any published research on Dominion, but there is a [simulator](h
- [MDA: A Formal Approach to Game Design and Game Research ](https://www.aaai.org/Papers/Workshops/2004/WS-04-04/WS04-04-001.pdf)
# Frameworks/Toolkits
- [RLCard: A Toolkit for Reinforcement Learning in Card Games](https://arxiv.org/abs/1910.04376)
- [RLCard: A Toolkit for Reinforcement Learning in Card Games](https://arxiv.org/abs/1910.04376)