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» The Learning Power of Evolution
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148
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AROBOTS
1999
104views more  AROBOTS 1999»
15 years 3 months ago
Reinforcement Learning Soccer Teams with Incomplete World Models
We use reinforcement learning (RL) to compute strategies for multiagent soccer teams. RL may pro t signi cantly from world models (WMs) estimating state transition probabilities an...
Marco Wiering, Rafal Salustowicz, Jürgen Schm...
GECCO
2010
Springer
212views Optimization» more  GECCO 2010»
15 years 8 months ago
Generative and developmental systems
This paper argues that multiagent learning is a potential “killer application” for generative and developmental systems (GDS) because key challenges in learning to coordinate ...
Kenneth O. Stanley
142
Voted
SAB
2010
Springer
117views Optimization» more  SAB 2010»
15 years 2 months ago
Indirectly Encoding Neural Plasticity as a Pattern of Local Rules
Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve t...
Sebastian Risi, Kenneth O. Stanley
AGILEDC
2005
IEEE
15 years 9 months ago
Experiences Teaching a Course in Programmer Testing
We teach a class on programmer-testing with a primary focus on test-driven development (TDD) as part of the software engineering curriculum at the Florida Institute of Technology....
Andy Tinkham, Cem Kaner
116
Voted
PDP
2003
IEEE
15 years 9 months ago
A multi-tiered agent-based architecture for a cooperative learning environment
: In this paper the problem of educational resource management in a cooperative learning environment is discussed. A task model was elaborated to determine both functional and leve...
Eduardo Sánchez Vila, Manuel Lama, Ricardo ...