Qualitative Velocity and Ball Interception

Frieder Stolzenburg, Oliver Obst, and Jan Murray. Qualitative Velocity and Ball Interception. In Matthias Jarke, Jana Köhler, and Gerhard Lakemeyer, editors, KI-2002: Advances in Artificial Intelligence -- Proceedings of the 25th Annual German Conference on Artificial Intelligence, number 2479 in Lecture Notes in Artificial Intelligence, pages 283-298, Berlin, Heidelberg, New York, 2002. Springer.
Preliminary version appeared as Fachberichte Informatik 4/2002, Universität Koblenz-Landau.

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In many approaches for qualitative spatial reasoning, navigation of an agent in a more or less static environment is considered (e.g. in the double-cross calculus). However, in general, real environment are dynamic, which means that both the agent itself and also other objects and agents in the environment may move. Thus, in order to perform spatial reasoning, not only (qualitative) distance and orientation information is needed, but also information about (relative) velocity of objects. Therefore, we will introduce concepts for qualitative and relative velocity: (quick) to left, neutral, (quick) to right. We investigate the usefulness of this approach in a case study, namely ball interception of simulated soccer agents in the RoboCup. We compare a numerical approach where the interception point is computed exactly, a strategy based on reinforcement learning, a method with qualitative velocities developed in this paper, and the na{\"\i}ve method where the agent simply goes directly to the actual ball position.

@inproceedings{SOM02b,
Abstract = {In many approaches for qualitative spatial reasoning,
navigation of an agent in a more or less static environment is considered (e.g. in the double-cross calculus). However,
in general, real environment are dynamic, which means that both the agent itself and also other objects and agents in the environment may move. Thus, in order to perform spatial reasoning, not only (qualitative) distance and orientation information is needed, but also information about (relative) velocity of objects. Therefore, we will introduce concepts for qualitative and relative velocity: (quick) to left, neutral, (quick) to right. We investigate the usefulness of this approach in a case study, namely ball interception of simulated soccer agents in the RoboCup. We compare a numerical approach where the interception point is computed exactly, a strategy based on reinforcement learning, a method with qualitative velocities developed in this paper, and the na{\"\i}ve method where the agent simply goes directly to the actual ball position. },
Address = {Berlin, Heidelberg, New York},
Author = {Frieder Stolzenburg and Oliver Obst and Jan Murray},
Bib2Html_Sel = {selected},
Booktitle = {KI-2002: Advances in Artificial Intelligence -- Proceedings of the 25th Annual German Conference on Artificial Intelligence},
Date-Added = {2008-01-29 17:28:53 +1100},
Date-Modified = {2008-01-29 17:28:53 +1100},
Editor = {Matthias Jarke and Jana K{\"o}hler and Gerhard Lakemeyer},
Html = {http://link.springer-ny.com/link/service/series/0558/bibs/2479/24790283.htm},
Number = {2479},
Pages = {283--298},
Publisher = {Springer},
Series = lnai,
Title = {Qualitative Velocity and Ball Interception},
Wwwnote = {<a href="http://www.uni-koblenz.de/fb4/publikationen/gelbereihe/RR-12-01.pdf">Preliminary&nbsp;version</a> appeared as Fachberichte Informatik 4/2002, Universit{\"a}t Koblenz-Landau.},
Year = {2002},
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