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» Learning Heuristic Functions from Relaxed Plans
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JAIR
2006
111views more  JAIR 2006»
14 years 9 months ago
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning prob...
Vadim Bulitko, Greg Lee
ECAI
2010
Springer
14 years 11 months ago
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning...
Reinaldo A. C. Bianchi, Ramon López de M&aa...
COLT
2008
Springer
14 years 11 months ago
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weak-learnability. The starting po...
Shai Shalev-Shwartz, Yoram Singer
RSS
2007
151views Robotics» more  RSS 2007»
14 years 11 months ago
Predicting Partial Paths from Planning Problem Parameters
— Many robot motion planning problems can be described as a combination of motion through relatively sparsely filled regions of configuration space and motion through tighter p...
Sarah Finney, Leslie Pack Kaelbling, Tomás ...
RAS
2010
117views more  RAS 2010»
14 years 8 months ago
Extending BDI plan selection to incorporate learning from experience
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI exe...
Dhirendra Singh, Sebastian Sardiña, Lin Pad...