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FLAIRS
2004
15 years 9 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
APPINF
2003
15 years 9 months ago
A Multithreaded Compiler Backend for High-level Array Programming
Whenever large homogeneous data structures need to be processed in a non-trivial way, e.g. in computational sciences, image processing, or system simulation, high-level array prog...
Clemens Grelck
AAAI
1992
15 years 8 months ago
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exp...
Thomas Dean, Dana Angluin, Kenneth Basye, Sean P. ...
AIIDE
2009
15 years 8 months ago
Improving Offensive Performance Through Opponent Modeling
Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In thi...
Kennard Laviers, Gita Sukthankar, David W. Aha, Ma...
CORR
2007
Springer
72views Education» more  CORR 2007»
15 years 7 months ago
WACA: A Hierarchical Weighted Clustering Algorithm optimized for Mobile Hybrid Networks
—Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. In a dynamic environment—in terms of node mobility as well as in ter...
Matthias R. Brust, Adrian Andronache, Steffen Roth...