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Integer Programming Approaches for Automated Planning. Menkes van den Briel Department of Industrial Engineering Arizona State University [email protected] http://www.public.asu.edu/~dbvan1/. What is automated planning?. Ordering problem

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Integer programming approaches for automated planning

Integer Programming Approaches for Automated Planning

Menkes van den BrielDepartment of Industrial EngineeringArizona State [email protected]://www.public.asu.edu/~dbvan1/


What is automated planning
What is automated planning?

Ordering problem

Scheduling is the problem of deciding when to execute a set of actions

NP-complete

Selection and ordering problem

Planning is deciding both what actions need to be done and when to execute them

PSPACE-complete

Scheduling

Planning


What is automated planning1
What is automated planning?

  • Creating a computer program to produce a plan, a sequence of actions that will transform the world from some given initial state to a desired goal state

1

2

1

2

Initial states0 S

Goalg S

PlanP = a1, …, an

Action

Actions are state transformation functions


What is automated planning2
What is automated planning?

  • Creating a computer program to produce a plan, a sequence of actions that will transform the world from some given initial state to a desired goal state

Initial states0 S

Goalg S

PlanP = a1, …, an

Action

si

sj

Actions are state transformation functions


Planning applications
Planning applications

  • Autonomous vehicles

    • Mars rovers

    • Underwater robotics

    • Remote agent experiment

  • Games

    • Bridge Baron

    • General game playing

  • Others

    • Manufacturing process planning

    • Composition of web services

    • Cyber Security


Planning by integer programming
Planning by integer programming

Operations research (OR)

Scheduling problems typically involve solving hard optimization problems

Integer programming (IP), branch-and-bound

Artificial intelligence (AI)

Planning problems typically involve solving hard feasibility problems

Constraint satisfaction, satisfiability (SAT), A* search

Scheduling

Planning


Planning by integer programming1
Planning by integer programming

  • Very little focus on integer programming approaches for planning

    • [Bylander, 1997]

    • [Bockmayr and Dimopoulos, 1998, 1999]

    • [Kautz and Walser, 1999]

    • [Vossen et al., 1999]

    • [Dimopoulos, 2001]

    • [Dimopoulos and Gerevini, 2002]


Why this lack of interest
Why this lack of interest?

  • IP-based approaches simply don’t work

    • “Lplan [a linear programming-based heuristic for optimal planning] was often slower than the other algorithms primarily due to the time to evaluate the linear programming heuristic”[Bylander, 1997]

  • SAT-based approaches are much faster

    • SAT-based planners have successfully participated in IPC1, IPC2, IPC4, and IPC5

  • Traditionally there has been little focus on plan quality

    • Planning is PSPACE-complete, so finding a feasible plan is already hard enough


Counter arguments
Counter arguments

  • IP-based approaches do work

    • Optiplan, first IP-based planner to take part in the IPC series

    • Ranked 2nd in four out of seven domains in IPC4 in the optimal track for propositional domains

  • IP-based approaches can compete with SAT-based approaches

    • Represent planning as a set of interdependent network flow problems

    • Generalize the notion of action parallelism

  • Shift in focus towards optimal planning

    • Applied formulations to partial satisfaction planning problems

    • Developed a novel framework for optimal planning

    • Utilized LP relaxations in deriving quality sensitive heuristics


Contributions
Contributions

  • IP-based approaches do work

  • IP-based approaches can compete with SAT-based approaches

  • Shift in focus towards optimal planning

  • [Van den Briel, and Kambhampati. Journal of Artificial Intelligence Research, 2005]

  • [Van den Briel, Vossen, and Kambhampati. ICAPS, 2005]

  • [Van den Briel, Vossen, and Kambhampati. Journal of Artificial Intelligence Research, 2008]

  • [Van den Briel, et al. AAAI, 2004]

  • [Do, Benton, van den Briel, and Kambhampati. IJCAI, 2007]

  • [J. Benton, van den Briel, and Kambhampati. ICAPS, 2007]

  • [Van den Briel, Benton, Kambhampati, and Vossen. CP, 2007]


1 ip approaches do work
1. IP approaches do work

  • Optiplan

    • IP-based planner that extends the state change formulation by [Vossen et al., 1999]

[van den Briel, and Kambhampati, 2005]


Summary of results
Summary of results

  • International planning competition (IPC)

    • Bi-annual event

    • Provides data sets (domains) that are used as benchmarks

  • IPC4

    • 7 competition domains

    • 7 participating planners in the “optimal” track

  • Domains

    • Pipesworld

      • Control the flow of oil derivatives through a pipeline network, obeying various constraints such as product compatibility and tankage restrictions

    • Satellite

      • Collect image data with a number of satellites

    • Philosophers, Optical telegraph

      • Involves finding deadlocks in communication protocols



2 ip versus sat approaches
2. IP versus SAT approaches

  • Represent planning as a set of interdependent network flow problems

    • One network flow problem for each state variable in the planning domain

    • Nodes correspond to the values of the state variables, arcs correspond to the value transitions

  • Generalize the notion of action parallelism

    • Reduces the plan length of the solution plan (and thus the size of the formulation)


Logistics example
Logistics example

1

2

P

T

Truck

Load(P,T,1)Unload(P,T,1)

1

Drive(1,2)

Drive(2,1)

2

Load(P,T,1)Unload(P,T,1)

Package

1

Load(P,T,1)

unload(P,T,1)

2

Load(P,T,2)

unload(P,T,2)

T

States are described by state variables


Logistics example1
Logistics example

1

2

Prevail

Truck

Load(P,T,1)Unload(P,T,1)

1

Drive(1,2)

Drive(2,1)

2

Load(P,T,1)Unload(P,T,1)

Package

1

Load(P,T,1)

unload(P,T,1)

Effect

2

Load(P,T,2)

unload(P,T,2)

T

Actions are state transformation functions


One state change 1sc
One state change (1SC)

  • Network representation

  • Logistics example

Prevail

f

f

f

Effect

g

g

g

h

h

h

Plan step

Truck

1

1

2

2

Planning involves considering

plans of increasing length

Package

1

1

2

2

t

t

t = 1


One state change 1sc1
One state change (1SC)

  • Network representation

  • Logistics example

Prevail

f

f

f

Effect

g

g

g

h

h

h

Load(P,T,1)

Drive(1,2)

Unload(P,T,2)

Truck

1

1

1

1

2

2

2

2

Load(P,T,1)

-

Unload(P,T,2)

Package

1

1

1

1

2

2

2

2

t

t

t

t

t = 1

t = 2

t = 3


1sc formulation
1SC formulation

  • Constraints

    • State changes (network flow), for allc  CgCycf,g,t= 1{f  I} for f  DchCycg,h,t+1= fCycg,h,t for f  Dc , 1  t < T fCycf,g,T= 1 for g  G

    • Effect implications, for allc  C, 1  t  TaA:(f,g)SC(a)xa,t = ycf,g,tfor f, g  Dc, f  g xa,t  ycf,f,tfor a  A, f PR(a)


Summary of results2
Summary of results

  • Experimental setup

    • Domains from IPC2, IPC3

    • Comparing 1SC formulation versus SATPLAN04 (winner of the “optimal“ track IPC4)

    • 2.67GHz CPU with 1.0GB memory

  • Domains

    • Logistics, Driverlog

      • Involves driving trucks (and flying airplanes) around to deliver packages between locations

    • Blocksworld

      • Stacking and unstacking towers of blocks

    • Zenotravel

      • Transporting people around in planes, using different modes of movement: fast and slow



2 ip versus sat approaches1
2. IP versus SAT approaches

  • Represent planning as a set of interdependent network flow problems

    • One network flow problem for each state variable in the planning domain

    • Nodes correspond to the values of the state variables, arcs correspond to the value transitions

  • Generalize the notion of action parallelism

    • Reduces the plan length of the solution plan (and thus the size of the formulation)


Generalized one state change g1sc
Generalized one state change (G1SC)

  • Network representation

  • Example

Prevail

f

f

f

Effect

g

g

g

h

h

h

Load(P,T,1)Drive(1,2)

Unload(P,T,2)

Truck

1

1

1

2

2

2

Load(P,T,1)

Unload(P,T,2)

Package

1

1

1

2

2

2

t

t

t

t = 1

t = 2


Implied precedences g1sc
Implied precedences (G1SC)

  • Example

A4

A1

A3

A1,A2

A3

A4

A2

Implied precendence graph


Implied precedences g1sc1
Implied precedences (G1SC)

  • Example

  • Ordering (cycle elimination) constraints ensure a feasible ordering of the actions

A4

A1

A3

A1,A2

A3

A4

A2

Implied precendence graph

A4

A1

xA1,t + xA3,t + xA4,t  2


G1sc formulation
G1SC formulation

  • Constraints

    • State changes (network flow), for allc  CgCycf,g,t= 1{f  I} for f  DchCycg,h,t+1= fCycg,h,t for f  Dc, 1  t  T fCycf,g,T= 1 for g  G

    • Effect implications, for allc  C, 1 t  T

      aA:(f,f)SC(a)xa,t= ycf,g,t for f, g  Dc, f  g,

      xa,t  ycf,f,t + gDc:f≠g (ycg,f,t + ycf,g,t)for a  A, f PR(a)

    • Ordering (Cycle elimination) constraints

       aV()xa,t  |V()| – 1 for all cycles G, 1  t  T


Branch and cut
Branch-and-cut

START

STOP

Initialize LP

no

Nodes found?

yes

LP solver

Feasible?

no

Fathom

Node selection

yes

Z_lp < Z*?

no

yes

Cut generation

Cuts found?

yes

no

Integer?

no

Branching

yes


State change path pathsc
State change path (PathSC)

  • Network representation

  • Example

Prevail

f

f

f

Effect

g

g

g

h

h

h

Load(P,T,1)Drive(1,2)Unload(P,T,2)

Truck

1

1

2

2

load(P,T,1)unload(P,T,2)

Package

1

1

2

2

t

t

t = 1



Summary of results5
Summary of results

[van den Briel, Vossen, and Kambhampati, 2005, 2008]


3 shift towards optimal planning
3. Shift towards optimal planning

  • Applied formulations to partial satisfaction planning problems

  • Developed a novel framework for optimal planning

  • Utilized LP relaxations in deriving quality sensitive heuristic search approaches


Partial satisfaction planning
Partial satisfaction planning

  • PLAN LENGTH is PSPACE-complete

    • [Bylander, 1994]

  • PSP UTILITY COST is PSPACE-complete

    • [Van den Briel, et al., 2004]

Total Satisfaction Problems

PSP UTILITY COST

PSP NET BENEFIT

PLAN COST

PSP UTILITY

PSP GOAL LENGTH

Partial SatisfactionProblems

PLAN LENGTH

PSP GOAL

PLAN EXISTENCE


Framework for optimal planning
Framework for optimal planning

  • For step-based IP formulations optimality is restricted to the length of the plan

Plan step

Load(P,T,1)

Drive(1,2)

Unload(P,T,2)

Truck

1

1

1

1

2

2

2

2

Load(P,T,1)

-

Unload(P,T,2)

Package

1

1

1

1

2

2

2

2

t

t

t

t

t = 1

t = 2

t = 3


Framework for optimal planning1
Framework for optimal planning

1

2

P

T

Truck

Load(P,T,1)Unload(P,T,1)

1

Drive(1,2)

Drive(2,1)

2

Load(P,T,1)Unload(P,T,1)

Package

1

Load(P,T,1)

unload(P,T,1)

2

Load(P,T,2)

unload(P,T,2)

T


Action selection formulation
Action selection formulation

  • Variables

    • xa Z+, for a  A; xa is equal to the number of times action a is executed

    • yv(c,a) Z+, for v  V, a  A, a  –(c); yv(c,a) is equal to the number of times transition v(c,a) is executed

  • Objective function

    • MIN aAcaxa

  • Constraints

    • av+(e)yv(c,a) – a v–(e)yv(c,a)

    • av+(e)yv(c,a) = xa

No time indicesNo upper bounds

1 if c c0,v, c g–1 if c= c0,v, c g0 otherwise


Concurrent automata
Concurrent automata

  • Given a set of state variables V = {v1, …, vn}

  • For each v V we define a deterministic automaton Gv = (Dv, Av, v, v, c0,v, gv)

    • Dv is a finite set of states corresponding to the domain of state variable v

    • Av is a finite set of actions associated with the transitions in Gv

    • v : Dv  A  Dv is the transition function

    • v : Dv  2A is the active action function

    • c0,v  S is the initial state of state variable v

    • gv  S is a set of goal states of state variable v


Parallel composition
Parallel composition

  • The parallel composition of the two automata G1 and G2 is the automaton G1||G2 := (D1D2, A1A2, 1||2, 1||2, (c0,1, c0,2), g1g2)

    • 1||2((c1,c2),a) :=

    • 1||2(c1,c2) := [1(c1)2(c2)]  [1(c1)\A2][2(c2)\A1]

(1(c1,a), 2(c2,a) if a  1(c1)2(c2)(1(c1,a), c2) if a  1(c1)\A2(c1,2(c2,a)) if a  2(c2)\A1undefined otherwise


Logistics example2
Logistics example

1

2

P

T

Truck

Load(P,T,1)Unload(P,T,1)

1

Drive(1,2)

Drive(2,1)

2

Load(P,T,1)Unload(P,T,1)

Package

1

Load(P,T,1)

unload(P,T,1)

2

Load(P,T,2)

unload(P,T,2)

T


Simple logistics example
Simple logistics example

1

2

P

T

Truck|| Package

2,1

Drive(2,1)

Drive(1,2)

1,2

1,1

Drive(2,1)

Drive(1,2)

Unload(P, T,1)

Load(P, T,1)

2,2

1,T

Unload(P, T,2)

Drive(2,1)

Load(P, T,2)

Drive(1,2)

2,T


Summary of results6
Summary of results

Highlighted values equal optimal solution



Utilize lp in heuristic search
Utilize LP in heuristic search

BBOP-LP planner

[Benton, van den Briel, and Kambhampati, 2007]


Summary
Summary

  • IP-based approaches do work

    • Optiplan, first IP-based planner to take part in the IPC series

    • Ranked 2nd in four out of seven domains in IPC4 in the optimal track for propositional domains

  • IP-based approaches can compete with SAT-based approaches

    • Represent planning as a set of interdependent network flow problems

    • Generalize the notion of action parallelism

  • Shift in focus towards optimal planning

    • Applied formulations to partial satisfaction planning problems

    • Developed a novel framework for optimal planning

    • Utilized LP relaxations in deriving quality sensitive heuristics


Publications status
Publications status

  • Journal

    • [M.H.L. van den Briel, and S. Kambhampati. Optiplan: Unifying IP-based and graph-based planning. Journal of Artificial Intelligence Research, 24:623-635, 2005]

    • [M.H.L van den Briel, T. Vossen, and S. Kambhampati. Loosely coupled formulation for automated planning: An integer programming perspective. Journal of Artificial Intelligence Research, 31:217-257, 2008]

    • [(In progress) M.H.L van den Briel, T. Vossen, S. Kambhampati and J. Fowler. Optimal automated planning]

  • Conference

    • [M.H.L. van den Briel, R. Sanchez, M.B. Do, and S. Kambhampati. Effective approaches for partial satisfaction (oversubscription) planning. In Proceedings of AAAI, pages 562-569, 2004]

    • [M.H.L. van den Briel, T. Vossen, and S. Kambhampati. Reviving integer programming approaches for AI planning: A branch-and-cut framework. In Proceedings of ICAPS, pages 161-170, 2005]

    • [M.B. Do, J. Benton, M.H.L. van den Briel, and S. Kambhampati. Planning with goal utility dependencies. In Proceedings of IJCAI, pages 1872-1878, 2007]

    • [J. Benton, M.H.L. van den Briel, and S. Kambhampati. A hybrid linear programming and relaxed plan heuristic for partial satisfaction planning problems. In Proceedings of ICAPS, pages 24-41, 2007]

    • [M.H.L. van den Briel, J. Benton, S. Kambhampati, and T. Vossen. An LP-based heuristic for optimal planning. In Proceedings of CP, pages 651-665, 2007]

Cited by 6

Cited by 31

Cited by 15

Cited by 3

Cited by 4

Cited by 3


Publications status1
Publications status

  • Workshop and posters

    • [M.H.L. van den Briel, R. Sanchez, and S. Kambhampati. Over-Subscription in Planning: a Partial Satisfaction Problem. In Proceedings of ICAPS Workshop on Integrating Planning into Scheduling, 2005]

    • [M.H.L. van den Briel,. Kambhampati, and T. Vossen. Planning with numerical state variables through mixed integer programming. In Proceedings of ICAPS Poster Session, pages 5-8, 2005]

    • [M.H.L. van den Briel,. Kambhampati, and T. Vossen. Planning with preferences and trajectory constraints by integer programming. In Proceedings of ICAPS Workshop on Preferences and Soft Constraints in Planning, pages 19-22, 2006]

    • [J. Benton, M.H.L. van den Briel,. Kambhampati. Finding admissible bounds for oversubscription planning problems. In Proceedings of ICAPS Workshop on Heuristics for Domain-Independent Planning: Progress, Ideas, Limitations, Challenges, 2007]

    • [M.H.L. van den Briel,. Kambhampati, and T. Vossen. Fluent merging: A general technique to improve reachability heuristics and factored planning. In Proceedings of ICAPS Workshop on Heuristics for Domain-Independent Planning: Progress, Ideas, Limitations, Challenges, 2007]

Cited by 5

Cited by 1

Cited by 1

Citation count by Google Scholar


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