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» Evaluating Temporal Planning Domains
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ML
1998
ACM
136views Machine Learning» more  ML 1998»
13 years 7 months ago
Co-Evolution in the Successful Learning of Backgammon Strategy
Following Tesauro’s work on TD-Gammon, we used a 4000 parameter feed-forward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of t...
Jordan B. Pollack, Alan D. Blair
IJCAI
2003
13 years 9 months ago
In Defense of PDDL Axioms
There is controversy as to whether explicit support for PDDL-like axioms and derived predicates is needed for planners to handle real-world domains effectively. Many researchers h...
Sylvie Thiébaux, Jörg Hoffmann, Bernha...
AAAI
2011
12 years 7 months ago
A Switching Planner for Combined Task and Observation Planning
From an automated planning perspective the problem of practical mobile robot control in realistic environments poses many important and contrary challenges. On the one hand, the p...
Moritz Göbelbecker, Charles Gretton, Richard ...
ICCBR
2005
Springer
14 years 1 months ago
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time ...
David W. Aha, Matthew Molineaux, Marc J. V. Ponsen
RAS
2010
117views more  RAS 2010»
13 years 6 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...