Learning Symbolic Models of Stochastic Domains

Details Learning Symbolic Models of Stochastic Domains Learning Symbolic Models of Stochastic Domains

2011-10-10

arxiv.org/abs/1110.2211v1

Journal Of Artificial Intelligence Research, Volume 29, pages 309-352, 2007

Computer Science

Learning

Scientific paper

10.1613/jair.2113

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

Affiliated with

Also associated with

No associations

Rating

Learning Symbolic Models of Stochastic Domains does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Learning Symbolic Models of Stochastic Domains, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Learning Symbolic Models of Stochastic Domains will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-471078