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ATAL
2007
Springer
14 years 1 months ago
Model-based function approximation in reinforcement learning
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Nicholas K. Jong, Peter Stone
ECML
2004
Springer
14 years 25 days ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
ATAL
2006
Springer
13 years 11 months ago
Learning the task allocation game
The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators...
Sherief Abdallah, Victor R. Lesser
AAAI
2007
13 years 9 months ago
AURA: Enabling Subject Matter Experts to Construct Declarative Knowledge Bases from Science Textbooks
The long-term goal of Project Halo is to build an application called Digital Aristotle that can answer questions on a variety of science topics and provide user and domain appropr...
Ken Barker, Vinay K. Chaudhri, Shaw Yi Chaw, Peter...
AAAI
2007
13 years 9 months ago
Using AI for e-Government Automatic Assessment of Immigration Application Forms
This paper describes an e-Government AI project that provides a range of intelligent AI services to support automated assessment of various types of applications submitted to an i...
Andy Hon Wai Chun