Playing Chess with Limited Look Ahead

07/04/2020
by   Arman Maesumi, et al.
0

We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of this research lies with creating a chess engine that is highly capable, but restricted in its look ahead depth. We train a deep neural network to serve as a static evaluation function, which is accompanied by a relatively simple look ahead algorithm. We show that our static evaluation function has encoded some semblance of look ahead knowledge, and is comparable to classical evaluation functions. The strength of our chess engine is assessed by comparing its proposed moves against those proposed by Stockfish. We show that, despite strict restrictions on look ahead depth, our engine recommends moves of equal strength in roughly 83% of our sample positions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/31/2019

Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games

Depth-limited look-ahead search is an essential tool for agents playing ...
research
06/01/2011

Conflict-Directed Backjumping Revisited

In recent years, many improvements to backtracking algorithms for solvin...
research
03/18/2022

Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation

Level set estimation (LSE) is the problem of identifying regions where a...
research
12/11/2018

R3-DLA (Reduce, Reuse, Recycle): A More Efficient Approach to Decoupled Look-Ahead Architectures

Modern societies have developed insatiable demands for more computation ...
research
08/23/2012

Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree Policies and Direct Policy Search

Direct policy search (DPS) and look-ahead tree (LT) policies are two wid...
research
10/04/2018

An Efficient Approach for Removing Look-ahead Bias in the Least Square Monte Carlo Algorithm: Leave-One-Out

The least square Monte Carlo (LSM) algorithm proposed by Longstaff and S...
research
10/21/2015

GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present GLASSES: Global optimisation with Look-Ahead through Stochast...

Please sign up or login with your details

Forgot password? Click here to reset