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ATAL
2010
Springer
13 years 10 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
GECCO
2006
Springer
185views Optimization» more  GECCO 2006»
14 years 26 days ago
Robot gaits evolved by combining genetic algorithms and binary hill climbing
In this paper an evolutionary algorithm is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental appro...
Lena Mariann Garder, Mats Erling Høvin
AAAI
1998
13 years 10 months ago
A Motivational System for Regulating Human-Robot Interaction
This paper presents a motivational system for an autonomous robot which is designed to regulate human-robot interaction. The mode of social interaction is that of a caretaker-infa...
Cynthia Breazeal
CEC
2010
IEEE
13 years 10 months ago
Concurrently evolving sensor morphology and control for a hexapod robot
Evolving a robot's sensor morphology along with its control program has the potential to significantly improve its effectiveness in completing the assigned task, plus accommod...
Gary B. Parker, Pramod J. Nathan
AIPS
1994
13 years 10 months ago
Becoming Increasingly Reliable
Autonomousmobile robots need to detect potential failures reliably and react appropriately. Dueto uncertainties about the robots and their environment,it is extremelydifficult to ...
Reid G. Simmons