Abstract
The research question addressed in this paper is: Given a problem, can we automatically predict how difficult the problem will be to solve by humans? We focus our investigation on problems in which the difficulty arises from the combinatorial complexity of problems. We propose a measure of difficulty that is based on modeling the problem solving effort as search among alternatives and the relations among alternative solutions. In experiments in the chess domain, using data obtained from very strong human players, this measure was shown at a high level of statistical significance to be adequate as a genuine measure of difficulty for humans.
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Guid, M., Bratko, I. (2013). Search-Based Estimation of Problem Difficulty for Humans. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_131
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DOI: https://doi.org/10.1007/978-3-642-39112-5_131
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
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