Matching Observed with Empirical Reality --What you see is what you get?

Authors:
Vladimir Kurbalija;Mirjana Ivanović;Charlotte von Bernstorff;Jens Nachtwei;Hans-Dieter Burkhard
Affiliations:
Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg D. Obradovica 4, 21000 Novi Sad, Serbia. kurba@dmi.uns.ac.rs, mira@dmi.uns.ac.rs;Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg D. Obradovica 4, 21000 Novi Sad, Serbia. kurba@dmi.uns.ac.rs, mira@dmi.uns.ac.rs;Institute of Psychology, Humboldt University Berlin, Rudower Chaussee 18, 12489 Berlin, Germany. charlotte.bernstorff@hu-berlin.de, jens.nachtwei@hu-berlin.de;Institute of Psychology, Humboldt University Berlin, Rudower Chaussee 18, 12489 Berlin, Germany. charlotte.bernstorff@hu-berlin.de, jens.nachtwei@hu-berlin.de;Institute of Informatics, Humboldt University Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. hdb@informatik.hu-berlin.de
Venue:
Fundamenta Informaticae - Dedicated to the Memory of Professor Manfred Kudlek
Year:
2014

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Abstract

This paper outlines the primary steps to investigate if artificial agents can be considered as true substitutes of humans. Based on a Socially augmented microworld SAM human tracking behavior was analyzed using time series. SAM involves a team of navigators jointly steering a driving object along different virtual tracks containing obstacles and forks. Speed and deviances from track are logged, producing high-resolution time series of individual training and cooperative tracking behavior. In the current study 52 time series of individual tracking behavior on training tracks were clustered according to different similarity measures. Resulting clusters were used to predict cooperative tracking behavior in fork situations. Results showed that prediction was well for tracking behavior shown at the first and, moderately well at the third fork of the cooperative track: navigators switched from their trained to a different tracking style and then back to their trained behavior. This matches with earlier identified navigator types, which were identified on visual examination. Our findings on navigator types will serve as a basis for the development of artificial agents, which can be compared later to behavior of human navigators.