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Compilation Approaches to AI Planning 1

Compilation Approaches to AI Planning 1. Jos é Luis Ambite* Some slides are taken from presentations by Kautz and Selman. Please visit their websites: http://www.cs.washington.edu/homes/kautz/ http://www.cs.cornell.edu/home/selman/. Complexity of Planning.

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Compilation Approaches to AI Planning 1

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  1. Compilation Approaches to AI Planning 1 José Luis Ambite* Some slides are taken from presentations by Kautz and Selman. Please visit their websites: http://www.cs.washington.edu/homes/kautz/ http://www.cs.cornell.edu/home/selman/ CS 541

  2. Complexity of Planning • Domain-independent planning: PSPACE-complete or worse • (Chapman 1987; Bylander 1991; Backstrom 1993) • Bounded-length planning: NP-complete • (Chenoweth 1991; Gupta and Nau 1992) • Approximate planning: NP-complete or worse • (Selman 1994) CS 541

  3. Compilation Idea • Use any computational substrate that is (at least) NP-hard. • Planning as: • SAT: Propositional Satisfiability • SATPLAN, Blackbox (Kautz&Selman, 1992, 1996, 1999) • OBDD: Ordered Binary Decision Diagrams (Cimatti et al, 98) • CSP: Constraint Satisfaction • GP-CSP (Do & Kambhampati 2000) • ILP: Integer Linear Programming • Kautz & Walser 1999, Vossen et al 2000 • … CS 541

  4. Planning as SAT • Bounded-length planning can be formalized as propositional satisfiability (SAT) • Plan = model (truth assignment) that satisfies logical constraints representing: • Initial state • Goal state • Domain axioms: actions, frame axioms, … for a fixed plan length • Logical spec such that any model is a valid plan CS 541

  5. Architecture of a SAT-based planner • Problem • Description • Init State • Goal State • Actions Compiler (encoding) Simplifier (polynomial inference) CNF Increment plan length If unsatisfiable mapping CNF satisfying model Decoder Solver (SAT engine/s) Plan CS 541

  6. Parameters of SAT-based planner • Encoding of Planning Problem into SAT • General Limited Inference: Simplification • SAT Solver(s) CS 541

  7. Encodings of Planning to SAT • Discrete Time • Each proposition and action have a time parameter: • drive(truck1 a b) ~> drive(truck1 a b 3) • at(p a) ~> at(p a 0) • Common Axiom schemas: • INIT: Initial state completely specified at time 0 • GOAL: Goal state specified at time N • A => P,E: Action implies preconditions and effects CS 541

  8. Encodings of Planning to SATCommon Schemas Example • INIT: on(a b 0) ^ clear(a 0) ^ … • GOAL: on(a c 2) • A => P,E Move(x y z) pre: clear(x) ^ clear(z) ^ on(x y) eff: on(x z) ^ not clear(z) ^ not on(x y) Move(a b c 1) => clear(a 0) ^ clear(b 0) ^ on(a b 0) Move(a b c 1) => on(a c 2) ^not clear(a 2) ^ not clear(b 2) CS 541

  9. Encodings of Planning to SATFrame Axioms • Classical: (McCarthy & Hayes 1969) • state what fluents are left unchanged by an action • clear(d i-1) ^ move(a b c i) => clear(d i+1) • Problem: if no action occurs at step i nothing can be inferred about propositions at level i+1 • Sol: at-least-one axiom: at least one action occurs • Explanatory: (Haas 1987) • State the causes for a fluent change • clear(d i-1) ^ not clear(d i+1) => (move(a b d i) v move(a c d i) v … move(c Table d i)) CS 541

  10. Encodings of Planning to SATOperator Splitting • To reduce size of instantiated formula (#vars) • Normal = plan-length #actions objectsmax-act-arity • Split = plan-length #actions objects max-act-arity • Replaces: drive(truck1 LA SF 5) • With: (drive-arg1(truck1 5) ^ drive-arg2(LA 5) ^ drive-arg3(SF 5)) CS 541

  11. KS96 Encodings: Linear (sequential) • Same as KS92 • Initial and Goal States • Action implies both preconditions and its effects • Only one action at a time • Some action occurs at each time (allowing for do-nothing actions) • Classical frame axioms • Operator Splitting CS 541

  12. KS96 Encodings: Graphplan-based • Goal holds at last layer (time step) • Initial state holds at layer 1 • Fact at level i implies disjuntion of all operators at level i–1 that have it as an add-efffect • Operators imply their preconditions • Conflicting Actions (only action mutex explicit, fact mutex implicit) CS 541

  13. Pre1 Act1 Fact Pre2 Act2 Graphplan Encoding Fact => Act1  Act2 Act1 => Pre1  Pre2 ¬Act1  ¬Act2 CS 541

  14. KS96 Encodings: State-based • Assert conditions for valid states • Combines graphplan and linear • Action implies both preconditions and its effects • Conflicting Actions (only action mutex explicit, fact mutex implicit) • Explanatory frame axioms • Operator splitting • Eliminate actions ( state transition axioms) CS 541

  15. Algorithms for SAT • Systematic (complete: prove sat and unsat) • Davis-Putnam (1960) • Satz (Li & Anbulagan 1997) • Rel-Sat (Bayardo & Schrag 1997) • Stochastic (incomplete: cannot prove unsat) • GSAT (Selman et al 1992) • Walksat (Selman et al 1994) • Randomized Restarts (Gomes et al 1998) CS 541

  16. Davis-Putnam algorithm function Satisfiable ( clause set S ) return boolean repeat /* unit propagation */ for each unit clause L in S do delete from S every clause containing L /* unit subsumption */ delete not L from every clause of S in which it occurs /*unit resolution*/ if S is empty then return true else if a clause becomes null in S then return false until no further changes result choose a literal L occurring in S /* splitting */ if Satisfiable ( S U L ) then return true else if Satisfiable ( S U {not L}) then return true else return false CS 541

  17. Walksat For i=1 to max-tries A:= random truth assigment For j=1 to max-flips If solution?(A) then return A else C:= random unsatisfied clause With probability p flip a random variable in C With probability (1- p) flip the variable in C that minimizes the number of unsatisfied clauses CS 541

  18. General Limited InferenceFormula Simplification • Generated wff can be further simplified by consistency propagation techniques • Compact(Crawford & Auton 1996) • unit propagation (unit clauses) O(n) • failed literal rule O(n2) • if Wff + { P } unsat by unit propagation, then set p to false • binary failed literal rule: O(n3) • if Wff + { P, Q } unsat by unit propagation, then add (not p V not q) • Experimentally reduces number of variables and clauses by 30% (Kautz&Selman 1999) CS 541

  19. Randomized Sytematic Solvers • Stochastic local search solvers (walksat) • when they work, scale well • cannot show unsat • fail on some domains • Systematic solvers (Davis Putnam) • complete • seem to scale badly • Can we combine best features of each approach? CS 541

  20. Heavy Tails • Bad scaling of systematic solvers can be caused by heavy tailed distributions • Deterministic algorithms get stuck on particular instances • but that same instance might be easy for a different deterministic algorithm! • Expected (mean) solution time increases without limit over large distributions CS 541

  21. Heavy Tailed Cost Distribution CS 541

  22. Randomized Restarts • Solution: randomize the systematic solver • Add noise to the heuristic branching (variable choice) function • Cutoff and restart search after a fixed number of backtracks • Eliminates heavy tails • In practice: rapid restarts with low cutoff can dramatically improve performance CS 541

  23. Rapid Restart Speedup CS 541

  24. Blackbox Results 1016 states 6,000 variables 125,000 clauses CS 541

  25. AI Planning Systems CompetitionCMU, 1998 Team Number of Average Fastest Shortest problems solution on solutions solved time (msec) for Blackbox 10 3171 3 6 (AT&T Labs) HSP 9 25875 1 5 (Venezuela) IPP 8 (11) 11036 1(3) 6(8) (Germany) STAN 7 20947 5 4 (UK) CS 541

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