Automatic interface optimization through random exploration of available elements

Authors:
Leonardo Anjoletto Ferreira;Andrey Araujo Masiero;Plinio Thomaz Aquino, Jr.;Reinaldo Augusto da Costa Bianchi
Affiliations:
Universidade Metodista de São Paulo, São Paulo - Brazil and Centro Universitário da FEI, São Paulo - Brazil;Centro Universitário da FEI, São Paulo - Brazil;Centro Universitário da FEI, São Paulo - Brazil;Centro Universitário da FEI, São Paulo - Brazil
Venue:
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
Year:
2013

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Abstract

The Keystroke-Level Model (KLM) is an interface evaluation method that use as metric the time needed to perform an executed action to complete a given task. The description used in KLM is very similar to the formalism that Markov Decision Process (MDP) uses to describe a domain, in which an artificial agent must perform a sequence of actions in order to solve a problem. This work presents a way to model a user's interaction with an interface using MDP combined with KLM in order to optimize a set of parameters and find the best set of interface components for a user. Results show that by changing the metrics of the KLM, the MDP finds different solutions that may be combined to generate an interface tailored for a given user.