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» Learning behavior styles with inverse reinforcement learning
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ICCAD
2008
IEEE
107views Hardware» more  ICCAD 2008»
14 years 2 months ago
Importance sampled circuit learning ensembles for robust analog IC design
This paper presents ISCLEs, a novel and robust analog design method that promises to scale with Moore’s Law, by doing boosting-style importance sampling on digital-sized circuit...
Peng Gao, Trent McConaghy, Georges G. E. Gielen
ATAL
2009
Springer
14 years 2 months ago
Adaptive learning in evolving task allocation networks
In this paper, we study multi-agent economic systems using a recent approach to economic modeling called Agent-based Computational Economics (ACE): the application of the Complex ...
Tomas Klos, Bart Nooteboom
AAAI
2000
13 years 9 months ago
ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction
We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of a...
Joseph Beck, Beverly Park Woolf, Carole R. Beal
CIA
2007
Springer
14 years 1 months ago
Agent Behavior Alignment: A Mechanism to Overcome Problems in Agent Interactions During Runtime
When two or more agents interacting, their behaviors are not necessarily matching. Automated ways to overcome conicts in the behavior of agents can make the execution of interacti...
Gerben G. Meyer, Nicolae B. Szirbik
SMC
2007
IEEE
102views Control Systems» more  SMC 2007»
14 years 2 months ago
An improved immune Q-learning algorithm
—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...