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EMO
2001
Springer
107views Optimization» more  EMO 2001»
15 years 11 months ago
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local optima - a state where no small modi cation of the curren...
Joshua D. Knowles, Richard A. Watson, David Corne
ICRA
2008
IEEE
124views Robotics» more  ICRA 2008»
16 years 1 months ago
Simultaneous learning of motion and sensor model parameters for mobile robots
— Motion and sensor models are crucial components in current algorithms for mobile robot localization and mapping. These models are typically provided and hand-tuned by a human o...
Teddy N. Yap Jr., Christian R. Shelton
AGENTS
1997
Springer
15 years 11 months ago
Learning View Graphs for Robot Navigation
Abstract. We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surr...
Matthias O. Franz, Bernhard Schölkopf, Philip...
AIPS
1994
15 years 8 months ago
Robot Motion Planning Integrating Planning Strategies and Learning Methods
Robot motion planning in a dynamic cluttered workspace requires the capability of dealing with obstacles and deadlock situations. The paper analyzes situations where the robot is ...
Luca Maria Gambardella, Cristina Versino
JACIII
2006
97views more  JACIII 2006»
15 years 6 months ago
Opposition-Based Reinforcement Learning
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropri...
Hamid R. Tizhoosh