Qualitative reasoning with higher-order derivatives
Readings in qualitative reasoning about physical systems
Qualitative representation of positional information
Artificial Intelligence
Semi-quantitative system identification
Artificial Intelligence
An architecture for action selection in robotic soccer
Proceedings of the fifth international conference on Autonomous agents
Real-World Applications of Qualitative Reasoning
IEEE Expert: Intelligent Systems and Their Applications
From Motion Observation to Qualitative Motion Representation
Spatial Cognition II, Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2000: Robot Soccer World Cup IV
Towards a Logical Approach for Soccer Agents Engineering
RoboCup 2000: Robot Soccer World Cup IV
A new minirobotics system for teaching and researching agent-based programming
CATE '07 Proceedings of the 10th IASTED International Conference on Computers and Advanced Technology in Education
Representing moving objects in computer-based expert systems: the overtake event example
Expert Systems with Applications: An International Journal
CBR for state value function approximation in reinforcement learning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
A qualitative trajectory calculus and the composition of its relations
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
Reasoning with qualitative velocity: towards a hybrid approach
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Ball interception behaviour in robotic soccer
Robot Soccer World Cup XV
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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 [12]). 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 (as e.g. in [1]), but also information about (relative) velocity of objects (see e.g. [2]). 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 [10]. 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茂ve method where the agent simply goes directly to the actual ball position.