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
Dataset Files
| File | Size |
|---|---|
| moral.data | 119.7 KB |
| moral.info | 2.2 KB |
| moral.theory | 2.1 KB |
| Index | 150 Bytes |
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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. & Daley, J. (1990). Moral Reasoner [Dataset]. UCI Machine Learning Repository. 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.