Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Angelic Hierarchical Planning: Optimal and Online Algorithms

Bhaskara Marthi, Stuart J. Russell and Jason Wolfe

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2008-150
December 6, 2008

http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-150.pdf

High-level actions (HLAs) are essential tools for coping with the large search spaces and long decision horizons encountered in real-world decision making. In a recent paper, we proposed an "angelic" semantics for HLAs that supports proofs that a high-level plan will (or will not) achieve a goal, without first reducing the plan to primitive action sequences. This paper extends the angelic semantics with cost information to support proofs that a high-level plan is (or is not) optimal. We describe the Angelic Hierarchical A* algorithm, which generates provably optimal plans, and show its advantages over alternative algorithms. We also present the Angelic Hierarchical Learning Real-Time A* algorithm for situated agents, one of the first algorithms to do hierarchical lookahead in an online setting. Since high-level plans are much shorter, this algorithm can look much farther ahead than previous algorithms (and thus choose much better actions) for a given amount of computational effort. This is an extended version of a paper by the same name appearing in ICAPS '08.


BibTeX citation:

@techreport{Marthi:EECS-2008-150,
    Author = {Marthi, Bhaskara and Russell, Stuart J. and Wolfe, Jason},
    Title = {Angelic Hierarchical Planning: Optimal and Online Algorithms},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2008},
    Month = {Dec},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-150.html},
    Number = {UCB/EECS-2008-150},
    Abstract = {High-level actions (HLAs) are essential tools for coping with the large search spaces and long decision 
horizons encountered in real-world decision making. In a recent paper, we proposed an "angelic" semantics 
for HLAs that supports proofs that a high-level plan will (or will not) achieve a goal, without first reducing the 
plan to primitive action sequences. This paper extends the angelic semantics with cost information to support 
proofs that a high-level plan is (or is not) optimal. We describe the Angelic Hierarchical A* algorithm, which 
generates provably optimal plans, and show its advantages over alternative algorithms. We also present the 
Angelic Hierarchical Learning Real-Time A* algorithm for situated agents, one of the first algorithms to do 
hierarchical lookahead in an online setting. Since high-level plans are much shorter, this algorithm can look 
much farther ahead than previous algorithms (and thus choose much better actions) for a given amount of 
computational effort. This is an extended version of a paper by the same name appearing in ICAPS '08.}
}

EndNote citation:

%0 Report
%A Marthi, Bhaskara
%A Russell, Stuart J.
%A Wolfe, Jason
%T Angelic Hierarchical Planning: Optimal and Online Algorithms
%I EECS Department, University of California, Berkeley
%D 2008
%8 December 6
%@ UCB/EECS-2008-150
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-150.html
%F Marthi:EECS-2008-150