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» Reacting, Planning, and Learning in an Autonomous Agent
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IAT
2010
IEEE
13 years 5 months ago
Selecting Operator Queries Using Expected Myopic Gain
When its human operator cannot continuously supervise (much less teleoperate) an agent, the agent should be able to recognize its limitations and ask for help when it risks making...
Robert Cohn, Michael Maxim, Edmund H. Durfee, Sati...
PKDD
2009
Springer
102views Data Mining» more  PKDD 2009»
14 years 1 months ago
Relevance Grounding for Planning in Relational Domains
Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
Tobias Lang, Marc Toussaint
AROBOTS
2007
159views more  AROBOTS 2007»
13 years 7 months ago
Structure-based color learning on a mobile robot under changing illumination
— A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. To operate in the real world, autonomo...
Mohan Sridharan, Peter Stone
CEC
2010
IEEE
12 years 11 months ago
Co-evolutionary search path planning under constrained information-sharing for a cooperative unmanned aerial vehicle team
—Mobile cooperative sensor networks are increasingly used for surveillance and reconnaissance tasks to support domain picture compilation. However, efficient distributed informat...
Jean Berger, Jens Happe
ATAL
2010
Springer
13 years 8 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh