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» AI planning: solutions for real world problems
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JAIR
2008
148views more  JAIR 2008»
13 years 7 months ago
Learning Partially Observable Deterministic Action Models
We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the ac...
Eyal Amir, Allen Chang
AI
2010
Springer
13 years 4 months ago
Robust solutions to Stackelberg games: Addressing bounded rationality and limited observations in human cognition
How do we build algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human interaction, such...
James Pita, Manish Jain, Milind Tambe, Fernando Or...
ICRA
2009
IEEE
112views Robotics» more  ICRA 2009»
14 years 2 months ago
Combining planning and motion planning
Abstract— Robotic manipulation is important for real, physical world applications. General Purpose manipulation with a robot (eg. delivering dishes, opening doors with a key, etc...
Jaesik Choi, Eyal Amir
PRICAI
2004
Springer
14 years 27 days ago
Solving Over-Constrained Temporal Reasoning Problems Using Local Search
Temporal reasoning is an important task in many areas of computer science including planning, scheduling, temporal databases and instruction optimisation for compilers. Given a kno...
Matthew Beaumont, John Thornton, Abdul Sattar, Mic...
SARA
2005
Springer
14 years 1 months ago
Synthesizing Plans for Multiple Domains
Intelligent agents acting in real world environments need to synthesize their course of action based on multiple sources of knowledge. They also need to generate plans that smoothl...
Abdelbaki Bouguerra, Lars Karlsson