Chess (Domain Theories)
6 different domain theories for generating legal moves of chess
Dataset Characteristics
DomainTheory
Subject Area
Game
Associated Tasks

Feature Type

# Instances

# Features

Dataset Information
Additional Information
The six encoding are briefly described below: 1) chess_flann_new: Written by flann@cs.orst.edu. Employs a geometric representation for states, with each square designated by an X,Y coordinate and square connectivity computed by vectors. Generates legal moves by first generating peusdo moves then eliminating those that result in the moving player being in check. 2) chess_flann_wyl: Written by flann@cs.orst.edu. Employs a relational representation for states, with each square given a unique name and square connectivity computed by an enumeration of connected relations. Generates legal moves by first generating peusdo moves then eliminating those that result in the moving player being in check. 3) chess_russell_wyl: Originally written by Stuart Russell in MRS, translated into prolog by flann@cs.orst.edu. Employs a geometric representation for states, with each square designated by an X,Y coordinate and square connectivity computed by vectors. Generates legal moves by determining whether the moving side is in check. If the moving side is in check, moves are generated that destroy the check threat. If the moving side is not in check, moves are generated that do not create a check threat. Note that if the moving side is in check from multiple threats then the domain theory generates incorrect moves. 4) chess_vijay_1: Written by vijay@cs.orst.edu. Employs a relational representation for states, with each square given a unique name and square connectivity computed by an enumeration of connected relations. Generates legal moves by first generating peusdo moves then eliminating those that result in the moving player being in check. 5) chess_vijay_2: Written by vijay@cs.orst.edu. Employs a geometric representation for states, with each square designated by an X,Y coordinate and square connectivity computed by vectors. Generates legal moves by first generating peusdo moves then eliminating those that result in the moving player being in check. 6) chess_vijay_3: Written by vijay@cs.orst.edu. Employs a special linear representation for states, with each square designated by a single number and square connectivity computed by a single delta value. Generates legal moves by first generating peusdo moves then eliminating those that result in the moving player being in check. Each domain theory includes a sample state called state1 that describes the board position illustrated as Figure 4(d) in Flann and Dietterich, "A study of explanationbased methods for inductive learning" in Machine Learning, 4 187226. See file test_domain_theories for an example of loading and running the domain theories. In addition to the domain theories, a file called support_code is included that contains some useful prolog routines. One routine takes a generic chess board description and a domain theory name, and produces a prolog state description suitable for use with the given domain theory. See file test_domain_theories for an example of generating state descriptions.
Has Missing Values?
No
Russell,Stuart and Flann,Flann. Chess (Domain Theories). UCI Machine Learning Repository. https://doi.org/10.24432/C5PS35.
@misc{misc_chess_(domain_theories)_24, author = {Russell,Stuart and Flann,Flann}, title = {{Chess (Domain Theories)}}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: https://doi.org/10.24432/C5PS35} }
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset chess_domain_theories = fetch_ucirepo(id=24) # data (as pandas dataframes) X = chess_domain_theories.data.features y = chess_domain_theories.data.targets # metadata print(chess_domain_theories.metadata) # variable information print(chess_domain_theories.variables)
Creators
Stuart Russell
Flann Flann
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.