Human-Evolutionary Problem Solving through Gamification of a Bin-Packing Problem
Ross, N; Johns, M; Keedwell, EC; et al.Savic, D
Date: 13 July 2019
Conference paper
Publisher
Association for Computing Machinery (ACM)
Publisher DOI
Abstract
Many complex real-world problems such as bin-packing are
optimised using evolutionary computation (EC) techniques.
Involving a human user during this process can avoid producing
theoretically sound solutions that do not translate to the real world
but slows down the process and introduces the problem of user
fatigue. Gamification ...
Many complex real-world problems such as bin-packing are
optimised using evolutionary computation (EC) techniques.
Involving a human user during this process can avoid producing
theoretically sound solutions that do not translate to the real world
but slows down the process and introduces the problem of user
fatigue. Gamification can alleviate user boredom, concentrate user
attention, or make a complex problem easier to understand. This
paper explores the use of gamification as a mechanism to extract
problem-solving behaviour from human subjects through
interaction with a gamified version of the bin-packing problem,
with heuristics extracted by machine learning. The heuristics are
then embedded into an evolutionary algorithm through the
mutation operator to create a human-guided algorithm.
Experimentation demonstrates that good human performers
augment EA performance, but that poorer performers can be
detrimental to it in certain circumstances. Overall, the introduction
of human expertise is seen to benefit the algorithm.
Computer Science
Faculty of Environment, Science and Economy
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