First Order Decision Diagrams for Relational MDPs

Computer Science – Artificial Intelligence

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

10.1613/jair.2489

Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

First Order Decision Diagrams for Relational MDPs 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 First Order Decision Diagrams for Relational MDPs, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and First Order Decision Diagrams for Relational MDPs will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-466856

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.