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IJCAI
2001
13 years 10 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
ATAL
2010
Springer
13 years 10 months ago
Learning context conditions for BDI plan selection
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. In particular, the so-calle...
Dhirendra Singh, Sebastian Sardiña, Lin Pad...
CAEPIA
2009
Springer
13 years 10 months ago
Developing Strategies for the ART Domain
In this paper we propose the design of an agent for the ART Testbed, a tool created with the goal of objectively evaluate different trust strategies. The agent design includes a tr...
Javier Murillo, Víctor Muñoz, Beatri...
GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
13 years 10 months ago
Rank based variation operators for genetic algorithms
We show how and why using genetic operators that are applied with probabilities that depend on the fitness rank of a genotype or phenotype offers a robust alternative to the Sim...
Jorge Cervantes, Christopher R. Stephens
ALGORITHMICA
2007
70views more  ALGORITHMICA 2007»
13 years 9 months ago
Sampling Conformation Space to Model Equilibrium Fluctuations in Proteins
This paper proposes the Protein Ensemble Method (PEM) to model equilibrium fluctuations in proteins where fragments of the protein polypeptide chain can move independently of one ...
Amarda Shehu, Cecilia Clementi, Lydia E. Kavraki