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» A New Way to Introduce Knowledge into Reinforcement Learning
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IUI
2005
ACM
15 years 5 months ago
Task learning by instruction in tailor
In order for intelligent systems to be applicable in a wide range of situations, end users must be able to modify their task descriptions. We introduce Tailor, a system that allow...
Jim Blythe
ACIVS
2009
Springer
15 years 6 months ago
Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS
Abstract. Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-...
Huanzhang Fu, Zhongzhe Xiao, Emmanuel Dellandr&eac...
CCIA
2006
Springer
15 years 3 months ago
Learning from cooperation using justifications
In multi-agent systems, individual problem solving capabilities can be improved thanks to the interaction with other agents. In the classification problem solving task each agent i...
Eloi Puertas, Eva Armengol
AIIDE
2007
15 years 2 months ago
Automatic Rule Ordering for Dynamic Scripting
The goal of adaptive game AI is to enhance computercontrolled game-playing agents with (1) the ability to selfcorrect mistakes, and (2) creativity in responding to new situations....
Timor Timuri, Pieter Spronck, H. Jaap van den Heri...
ICCBR
2001
Springer
15 years 4 months ago
Meta-case-Based Reasoning: Using Functional Models to Adapt Case-Based Agents
It is useful for an intelligent software agent to be able to adapt to new demands from an environment. Such adaptation can be viewed as a redesign problem; an agent has some origin...
J. William Murdock, Ashok K. Goel