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ICML
2006
IEEE
16 years 4 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto
AIED
2007
Springer
15 years 10 months ago
Supporting Collaborative Idea Generation: A Closer Look Using Statistical Process Analysis Techniques
We are developing a conversational agent called VIBRANT to provide adaptive support for brainstorming in pairs in a scientific inquiry context. Our previous experimental study indi...
Hao-Chuan Wang, Carolyn Penstein Rosé
AAMAS
2007
Springer
15 years 10 months ago
Auctions, Evolution, and Multi-agent Learning
For a number of years we have been working towards the goal of automatically creating auction mechanisms, using a range of techniques from evolutionary and multi-agent learning. Th...
Steve Phelps, Kai Cai, Peter McBurney, Jinzhong Ni...
AGI
2011
14 years 7 months ago
Reinforcement Learning and the Bayesian Control Rule
We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intra...
Pedro Alejandro Ortega, Daniel Alexander Braun, Si...
ATAL
2003
Springer
15 years 7 months ago
Democracy in open agent systems
What sort of democracies should open agent societies be? We present three normative models of democracy from political philosophy and consider their relevance for the engineering o...
Peter McBurney, Simon Parsons