sat planning for mprimes

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SAT Planning Bounded Planning Problem MPrimes (AIPS 1998) Fariz Darari [email protected] FU Bolzano

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Page 1: SAT Planning for MPrimes

SAT Planning Bounded Planning Problem

MPrimes (AIPS 1998)

Fariz [email protected]

FU Bolzano

Page 2: SAT Planning for MPrimes

Formulas

1. Initial State2. Actions (Preconditions and Effects)3. Explanatory Frame Axiom4. Complete Exclusion Axiom5. Goal

Page 3: SAT Planning for MPrimes

Formulas

• To satisfy:initial state & all possible action descriptions & goal

1 & {2, 3, 4} & 5

Page 4: SAT Planning for MPrimes

1 - Initial State

• Specify what is true at the beginning (t = 0).– Example: (at v0 l0 0)

• Specify what is not true at the beginning (t = 0).– Example: (not (at v0 l1 0))

Page 5: SAT Planning for MPrimes

Dynamic vs Static

• We need to specify only the falsehood of dynamic states, but not of static states!

• Why?Possible actions will always satisfy the precondition for corresponding static states, since they are generated using the facts about static states themselves!

• In other words, it is safe to give an interpretation for any static state wrt. possible actions, to be true at a specific time (well since they are static!).

Page 6: SAT Planning for MPrimes

OWA

• Propositional logic has no CWA!– Therefore, what are not specified can be interpreted as True

(xor False).– Example: (random-predicate random-constant) can be

interpreted as true!• Therefore, if we ask for the satisfiability of:

(at v0 l0 0) & -(at v0 l1 0) & (asdf zxcv 0)The answer will still be YES!

• Do we need to add -(asdf zxcv 0)? No since we will never care about the value of (asdf zxcv 0) and never put it in the formula!

Page 7: SAT Planning for MPrimes

2 - Actions

• We want to represent the actions as compact as possible!• Characteristics:

– Action name– Preconditions– Effects– Typing

• All these characteristics define:Possible actions!

PS: We can think these possible actions as a substitution!

Page 8: SAT Planning for MPrimes

Example

(:action move :params (?v - vehicle ?l1 ?l2 - location ?f1 ?f2 - fuel) :precondition (and (at ?v ?l1) (conn ?l1 ?l2) (has-fuel ?l1 ?f1) (fuel-neighbor ?f2 ?f1)) :effect (and (not (at ?v ?l1)) (at ?v ?l2) (not (has-fuel ?l1 ?f1)) (has-fuel ?l1 ?f2)))

Page 9: SAT Planning for MPrimes

Move

• move (?v, ?l1, ?l2, ?f1, ?f2)• ?v is of type Vehicle• (at ?v ?l1) is in precondition, but do you think

we can use this knowledge benefit? No, since at is dynamic (time dependent)!

• Therefore, ?v ranges over objects defined as a vehicle without any other restrictions

Page 10: SAT Planning for MPrimes

Move

• move (?v, ?l1, ?l2, ?f1, ?f2)• ?l1 and ?l2 are of type Location• (conn ?l1 ?l2) is in precondition, but do you think

we can use this knowledge benefit? Yes, since conn is static (time independent)!

• Therefore, ?l1 and ?l2 ranges over object locations defined in conn!

• Another benefit: the precondition of conn is always satisfied!

Page 11: SAT Planning for MPrimes

Move

• move (?v, ?l1, ?l2, ?f1, ?f2)• ?f1 and ?f2 are of type Fuel• (fuel-neighbor ?f2 ?f1), beware of the order, is in

precondition, but do you think we can use this knowledge benefit? Yes, since fuel-neighbor is static!

• Therefore, ?f1 and ?f2 ranges over object fuels defined in fuel-neighbor!

• Another benefit: the precondition of fuel-neighbor is always satisfied!

Page 12: SAT Planning for MPrimes

How much reduction do we get?

• Suppose |v| = 10, |l| = 10, |f| = 10, |conn| = 2 * 10, |fuel-neighbor| = 10

• Naive encoding (typing) =n ^ 5 = 10 ^ 5 = 100.000

• Improved encoding =2 * (n ^ 3) = 10 * 2 * 10 * 10 = 2000

• Even worse, suppose n = 100, then:100 ^ 5 = 10.000.000.000 (☠)

• But with the improved encoding:100 * 2 * 100 * 100 = 2.000.000

Page 13: SAT Planning for MPrimes

Precondition

:precondition (and (at ?v ?l1) (conn ?l1 ?l2) (has-fuel ?l1 ?f1)

(fuel-neighbor ?f2 ?f1))

Page 14: SAT Planning for MPrimes

Relation between Precond and Possible Actions

• Action -> Precond• Example: move (?v, ?l1, ?l2, ?f1, ?f2) implies

(and (at ?v ?l1) (conn ?l1 ?l2) (has-fuel ?l1 ?f1)

(fuel-neighbor ?f2 ?f1))

Page 15: SAT Planning for MPrimes

Relation between Precond and Possible Actions

• Substitution: (v0, l1, l2, f1, f0)• Example: move (v0, l1, l2, f1, f0) implies

(and (at v0 l1) //dynamic (conn l1 l2) //static (has-fuel l1 f1) //dynamic

(fuel-neighbor f0 f1)) //static

Page 16: SAT Planning for MPrimes

Effects

:effect (and (not (at ?v ?l1)) (at ?v ?l2) (not (has-fuel ?l1 ?f1)) (has-fuel ?l1 ?f2)))

Page 17: SAT Planning for MPrimes

Relation between Effect and Possible Actions

• Action -> Effect• Example: move (?v, ?l1, ?l2, ?f1, ?f2) implies(and (not (at ?v ?l1)) (at ?v ?l2) (not (has-fuel ?l1 ?f1)) (has-fuel ?l1 ?f2)))

Page 18: SAT Planning for MPrimes

Relation between Precond and Possible Actions

• Substitution: (v0, l1, l2, f1, f0)• Example: move (v0, l1, l2, f1, f0) implies(and (not (at v0 l1)) //dynamic (at v0 l2) //dynamic (not (has-fuel l1 f1)) //dynamic (has-fuel l1 f0))) //dynamic

Page 19: SAT Planning for MPrimes

3 - Explanatory Frame Axiom

• Describe what doesn’t change between steps i and i + 1.• Two axioms for every possible dynamic state at every time

step i• Say that if s changes truth value between i and i+1 then

the action at step i must be responsible:– not (s, i) & (s, i + 1) -> BIG OR {(a, i)| e in EFF(s)} OR False– (s, i) & not (s, i + 1) -> BIG OR {(a, i)| e in EFF(-s)} OR False

• If s became true then some action must have added it,if s became false then some action must have deleted it!

Page 20: SAT Planning for MPrimes

Example

• at = (n ^ 2) * t * 2• (and (at v0 l0 2) (not (at v0 l0 3)) )

IMPLY(or (move v0 l0 l1 f1 f0 2) (false) )

• move (?v, ?l1, ?l2, ?f1, ?f2) Precondition = (at ?v ?l1)Effecct = (not (at ?v ?l1))

Page 21: SAT Planning for MPrimes

4 - Complete Exclusion Axiom

• Very simple but huge!• For all actions a and b and time steps i include

the formula ¬ (a, i) OR ¬ (b, i)

• This guaranteed that there could be only one action at a time

• Example: (or (not (unload c0 v0 l0 s0 s1 0)) (not (unload c0 v0 l1 s0 s1 0)))

Page 22: SAT Planning for MPrimes

5 - Goal

• Very simple!• Just a conjunction of your goals at the time n

(aka the bound).• Example (n = 20):

(and (at c0 l0 20) (at c1 l1 20) (at c2 l2 20))

Page 23: SAT Planning for MPrimes

DEMO

Example Solution:---PLAN---68 of load_c0_v0_l0_s1_s0_045 of move_v0_l0_l1_f2_f1_196 of unload_c0_v0_l1_s0_s1_2