Authors:
Tamal T. Biswas
and
Kenneth W. Regan
Affiliation:
University at Buffalo, United States
Keyword(s):
Decision Making, Depth of Search, Chess, Item Difficulty, Judging of Learning Agents, Knowledge Representation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Qualitative approaches to cognitive rigor and depth and complexity are broadly represented by Webb’s Depth
of Knowledge and Bloom’s Taxonomy. Quantitative approaches have been relatively scant, and some have
been based on ancillary measures such as the thinking time expended to answer test items. In competitive
chess and other games amenable to incremental search and expert evaluation of options, we show how depth
and complexity can be quantified naturally. We synthesize our depth and complexity metrics for chess into
measures of difficulty and discrimination, and analyze thousands of games played by humans and computers
by these metrics. We show the extent to which human players of various skill levels evince shallow versus
deep thinking, and how they cope with ‘difficult’ versus ‘easy’ move decisions. The goal is to transfer these
measures and results to application areas such as multiple-choice testing that enjoy a close correspondence in
form and item values to the problem of fin
ding good moves in chess positions.
(More)