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AI
2007
Springer
15 years 3 months ago
What evolutionary game theory tells us about multiagent learning
This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multiagent learning is the answer, what is the question? A...
Karl Tuyls, Simon Parsons
AAI
2005
138views more  AAI 2005»
15 years 3 months ago
Learning By Teaching: A New Agent Paradigm For Educational Software
This paper discusses Betty's Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep lear...
Gautam Biswas, Krittaya Leelawong, Daniel Schwartz...
TKDE
2010
224views more  TKDE 2010»
15 years 1 months ago
Probabilistic Topic Models for Learning Terminological Ontologies
—Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach f...
Wang Wei, Payam M. Barnaghi, Andrzej Bargiela
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 6 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
IJCAI
1989
15 years 4 months ago
A Critique of the Valiant Model
This paper considers the Valiant framework as it is applied to the task of learning logical concepts from random examples. It is argued that the current interpretation of this Val...
Wray L. Buntine