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Qualitative Velocity and Ball Interception


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


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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\"\ive method where the agent simply goes directly to the actual ball position.


BiBTeX Entry


@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},
  booktitle	= {KI-2002: Advances in Artificial Intelligence --
		   Proceedings of the 25th Annual German Conference on Artificial
		   Intelligence},
  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	= {Lecture Notes in Artificial Intelligence},
  title 	= {Qualitative Velocity and Ball Interception},
  wwwnote	= {P
		   reliminary version appeared as Fachberichte Informatik 4/2002,
		   Universit{\"a}t Koblenz-Landau.},
  year		= {2002},
}