Moral Reasoner
Donated on 5/31/1994
Horn-clause model that qualitatively simulates moral reasoning; Theory includes negated literals
Dataset Characteristics
Domain-Theory
Subject Area
Computer Science
Associated Tasks
-
Feature Type
-
# Instances
202
# Features
-
Dataset Information
Additional Information
This is a rule-based model that qualitatively simulates moral reasoning. The model was intended to simulate how an ordinary person, down to about age five, reasons about harm-doing. The horn-clause theory and the 202 instances are the same as were used in (Wogulis, 1994). The top-level predicate to predict is guilty/1. For more information, e.g. on the generation of instances, see (Wogulis, 1994).
Has Missing Values?
No
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset moral_reasoner = fetch_ucirepo(id=71) # data (as pandas dataframes) X = moral_reasoner.data.features y = moral_reasoner.data.targets # metadata print(moral_reasoner.metadata) # variable information print(moral_reasoner.variables)
Shultz,T.R. and Daley,J.M.. (1994). Moral Reasoner. UCI Machine Learning Repository. https://doi.org/10.24432/C56W47.
@misc{misc_moral_reasoner_71,
author = {Shultz,T.R. and Daley,J.M.},
title = {{Moral Reasoner}},
year = {1994},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C56W47}
}Creators
T.R. Shultz
J.M. Daley
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.