Efficient Planning, PDDL - GitHub Pages · HSP Heuristics H 0 - Cost of action is sum of...

9/9/2014S. Joo (sungmoon.joo@cc.gatech.edu) 1

CS 4649/7649Robot Intelligence: Planning

Sungmoon Joo

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Efficient Planning, PDDL

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*Slides based on Dr. Mike Stilman’s lecture slides

Course Info.

• Course Website: joosm.github.io/RIP2014 • Course Wiki: github.com/RIP2014/RIP2014Wiki/wiki

- add your contact info, start grouping/filling in project ideas, etc.• HW#1, due Sept. 29

Efficiency in Planning

Properties of Heuristics: h(s)

• Is A* with Admissible h(s) optimal?

Heuristics in Planning

Domain Independent Heuristics

HSP (Bonet & Geffner 1997)

Computing Costs of Literals

HSP Progression Heuristic

HSP Heuristics

- Cost of action is sum of precondition costs

- Informed, but not admissible

- Cost of Action is maximum over costs of preconditions- Admissible, but not very informed

- Solve for pairs of literals- Take maximum cost over all pairs- Informed, and claimed to be admissible

FF (Hoffman 2000)

Refines HSP Heuristics

FF Heuristic

FF vs. HSP

and admissible

FF vs. HSP

Plan Space Planning?

(reduces branching factor)

(Which subgoal(or threat) should be worked on?)

The Long Road

Propositional Satisfiability: Since 1960s

Planning As Satisfiability

Encoding Plans as SAT

only one action occurs at each time point

describe the predicate that are not affected by the actions

Solving SAT

The Last Classical Story

Who Won in 2008? FF/HSP Strike Back!

Who Won in 2011?

• Sequential Track: Winner - LAMA 2011Runner up - Stone Soup

Who Won in 2014?

67 planners in total from 66 people from 14 countries: Australia, Canada,Czech Republic, Finland, France, Germany, Iran, Israel, New Zealand,Spain, Switzerland, UK, Venezuela, and USA

*China(X), India(X), Italy(X)

• Sequential Optimal track: Winner - SymBA*-2, SymBA*-1

Runner up – cGamer• Sequential Multi-core track: Winner - ArvandHerd

Runner up – IBaCoP• Sequential Satisficing track: Winner – IBaCoP2

Runner up - Mercury• Agile track: Winner – YAHSP3

Runner up – Madagascar-pC• Temporal track: Winner – YAHSP3-MT

Runner up – Temporal Fast Downward

Summary

PDDL = Planning Domain Definition Language

• Components of a PDDL planning task:

-Objects: Things in the world- Predicates: Object properties- Initial state: The state of the world that we start in- Goal specification: Things that we want to be true- Actions/Operators: Ways of changing the state of the world

How to use PDDL for planning problems?

• Two files

- A domain file: predicates and actions- A problem file: objects, initial state and goal specification

(define (domain hanoi-domain)

(:requirements :equality)

(:predicates (disk ?x) (smaller ?x ?y) (on ?x ?y) (clear ?x))

(:action move-disk

:parameters (?disk ?below-disk ?new-below-disk)

:precondition (and (disk ?disk)

(smaller ?disk ?new-below-disk)

(not (= ?new-below-disk ?below-disk))

(not (= ?new-below-disk ?disk))

(not (= ?below-disk ?disk))

(on ?disk ?below-disk)

(clear ?disk)

(clear ?new-below-disk))

:effect (and (clear ?below-disk)

(on ?disk ?new-below-disk)

(not (on ?disk ?below-disk))

(not (clear ?new-below-disk)))))

How to use PDDL for planning problems?

• Two files

- A domain file: predicates and actions- A problem file: objects, initial state and goal specification

(define (problem hanoi-problem)(:domain hanoi-domain)(:objects p1 p2 p3 d1 d2 d3)(:init (smaller d1 p1) (smaller d2 p1) (smaller d3 p1)

(smaller d1 p2) (smaller d2 p2) (smaller d3 p2) (smaller d1 p3) (smaller d2 p3) (smaller d3 p3) (smaller d1 d2) (smaller d1 d3) (smaller d2 d3) (clear p1) (clear p2) (clear d1) (disk d1) (disk d2) (disk d3) (on d1 d2) (on d2 d3) (on d3 p3))(:goal (and (on d1 d2) (on d2 d3) (on d3 p1))))

• Domain description

(define (domain hanoi-domain)

(:requirements :equality)

(:predicates (disk ?x) (smaller ?x ?y) (on ?x ?y) (clear ?x))

(:action move-disk

:parameters (?disk ?below-disk ?new-below-disk)

:precondition (and (disk ?disk)

(smaller ?disk ?new-below-disk)

(not (= ?new-below-disk ?below-disk))

(not (= ?new-below-disk ?disk))

(not (= ?below-disk ?disk))

(on ?disk ?below-disk)

(clear ?disk)

(clear ?new-below-disk))

:effect (and (clear ?below-disk)

(on ?disk ?new-below-disk)

(not (on ?disk ?below-disk))

(not (clear ?new-below-disk)))))

Domain name

PredicatesActions

*Requirement flags allow a planner to quickly tell if it is likely to be able to hangle the domain

*Action effects can include universal quantifiers(i.e. forall), conditionals (e.g. when)

Requirements

• Problem descriptionProblem name

Objects

Initial state

(define (problem hanoi-problem)(:domain hanoi-domain)(:objects p1 p2 p3 d1 d2 d3)(:init (smaller d1 p1) (smaller d2 p1) (smaller

d3 p1) (smaller d1 p2) (smaller d2 p2) (smaller d3 p2) (smaller d1 p3) (smaller d2 p3) (smaller d3 p3) (smaller d1 d2) (smaller d1 d3) (smaller d2 d3) (clear p1) (clear p2) (clear d1) (disk d1) (disk d2) (disk d3) (on d1 d2) (on d2 d3) (on d3 p3))

(:goal (and (on d1 d2) (on d2 d3) (on d3 p1))))

Goal specification

*domain must match the corresponding domain name

Domain

Efficient Planning, PDDL - GitHub Pages · HSP Heuristics H 0 - Cost of action is sum of...

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