Search methods based on Monte-Carlo simulation

Add to your list(s) Download to your calendar using vCal

• Martin Müller, University of Alberta
• Thursday 05 August 2010, 14:00-15:00
• Small public lecture room, Microsoft Research Ltd, 7 J J Thomson Avenue (Off Madingley Road), Cambridge.

If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.

Search methods based on Monte-Carlo simulation have revolutionized difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk planning applies Monte-Carlo ideas to deterministic classical planning. The forward chaining planner Arvand uses such Monte-Carlo random walks to explore the local neighborhood of a search state for action selection. Random walks yield a large unbiased sample of the search neighborhood, and require expensive state evaluations only at the endpoints of each walk. The result is a relatively simple but powerful planner that is competitive with state of the art systems. The talk introduces the main ideas of planning using Monte-Carlo random walks, and briefly discusses recent work to improve plan quality and to improve planning with limited resources.

This talk is part of the Microsoft Research Cambridge, public talks series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Guy Emerson's list
• Interested Talks
• Microsoft Research Cambridge, public talks
• Optics for the Cloud
• PMRFPS's
• School of Technology
• Small public lecture room, Microsoft Research Ltd, 7 J J Thomson Avenue (Off Madingley Road), Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• personal list
• rp587

Note that ex-directory lists are not shown.