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Learning Applicability Conditions in AI Planning from Partial Observations

Learning Applicability Conditions in AI Planning from Partial Observations. Hankz Hankui Zhuo a , Derek Hao Hu a , Chad Hogg b , Qiang Yang a and Hector Munoz-Avila b a: Hong Kong University of Science & Technology, b: Lehigh University. Motivation.

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Learning Applicability Conditions in AI Planning from Partial Observations

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  1. Learning Applicability Conditions in AI Planning from Partial Observations Hankz Hankui Zhuoa, Derek Hao Hua, Chad Hoggb, Qiang Yanga and Hector Munoz-Avilab a: Hong Kong University of Science & Technology, b: Lehigh University

  2. Motivation • Modeling applicability conditions is difficult, especially for PDDL and HTN descriptions. • There are some learning algorithms based on complete state information. However, state information is often partial and noisy in some domains, e.g., • Batch commands in operating systems; • Activity recognition; • … • Our work focus on learning STRIPS model based on partial & noisy state information; and then extend it to PDDL and HTN model learning.

  3. Applicability Conditions Learning Hierarchies LAMP system HTN-learner HTN model PDDL model STRIPS Model ARMS system

  4. Notations • A planning domain: • A planning problem: • A plan trace: • The problem of learning applicability conditions is: • Input: a set of plan traces • Output: a set of applicability conditions so that plan traces are able to proceed.

  5. PDDL model HTN model STRIPS Model ARMS STRIPS Models Input: predicates, action schemas, a set of plan traces Output: STRIPS action models

  6. ARMS STRIPS Models Types, predicates, actions schemas Plan traces E.g., • The relation p must be generated by an action prior to p in the plan trace • The last action before p should not delete p • … Build constraints Solved w/ Weighted MAXSAT Calculate weights using frequent set mining algorithm, and solve these weighted constraints to finally attain action models Action models

  7. PDDL model HTN model STRIPS Model LAMPPDDL models Input: predicates, action schemas, a set of plan traces Output: action models with quantifiers and implications, e.g.,

  8. LAMPPDDL models Types, predicates, action schemas Plan traces Generate candidate formulas A set of propositions ``at’’ is a precondition of ``move’’ Learn weights of candidate formulas using MLNs Select formulas whose weights are larger than a threshold Convert the selected formulas to action models E.g. … Action models

  9. Input: a set of decomposition trees with partial observations, e.g. Output: action models and method preconditions. PDDL model HTN model STRIPS Model HTN-learner HTN models

  10. HTN-learner HTN models Decomposition trees Names, parameters, Tasks’ relations HTN schemata Relation information between States and methods/actions Build constraints State constraints Relation information between methods and actions Decomposition constraints Constraints imposed on action’s preconditions & effects Action constraints Solving constraints using weighted MAXSAT Solve constraints HTN model

  11. (:action pick-up (?x - block) :precondition (and(clear ?x)(ontable ?x)(handempty)) :effect (and (not (ontable ?x)) (not (clear ?x))(clear ?x) (not (handempty)) (handempty) (holding ?x))) (:action put-down (?x - block) :precondition (holding ?x) (clear ?x) :effect (and (not (holding ?x))(clear ?x) (handempty)(not (clear ?x))(ontable ?x))) (:action stack (?x - block ?y - block) :precondition (and (holding ?x)(clear ?y)(ontable ?y)) :effect (and (not (holding ?x)) (not (clear ?y)) (clear ?x)(handempty)(not(ontable ?y))(on ?x ?y))) (:action unstack (?x - block ?y - block) :precondition (and (on ?x ?y) (clear ?x) (handempty)) :effect (and (holding ?x) (clear ?y) (not (clear ?x)) (not (handempty))(on ?x ?y) (not (on ?x ?y)))) Experimental result (ARMS)

  12. (:action pick-up (?x - block) :precondition (and (clear ?x)(handempty)(holding ?x)) :effect (and (not (handempty)) (not(clear ?x)) (holding ?x) (when (ontable ?x)(not (ontable ?x))) (forall (?y-block) (when(on ?x ?y)(clear ?y))) (forall (?y-block)(when(on ?x ?y)(holding ?y))) (forall(?y-block) (when(on ?x ?y)(not(on ?x ?y)))))) (:action put-down (?x - block) :precondition (holding ?x) (clear ?x) (handempty) :effect (and (not (holding ?x))(clear ?x) (handempty) (ontable ?x) (forall (?y-block) (when (not(clear ?y)) (ontable ?x))) (forall (?y-block)(when (clear ?y) (on ?x ?y))))) (:action stack (?x - block ?y - block) :precondition (and (holding ?x) (clear ?y)(handempty)) :effect (and (not (holding ?x))(not (clear ?y))(clear ?x) (handempty) (on ?x ?y) (when (clear ?y)(on ?x ?y)) (when (ontable ?y)(on ?x ?y)) (when (ontable ?y)(not (clear ?y))) (when (not(clear ?y))(ontable ?x)))) (:action unstack (?x - block ?y - block) :precondition (and (clear ?x)(holding ?x)(handempty)) :effect (and(not(handempty)) (not(clear ?x))(ontable ?y) (clear ?x) (holding ?x) (when(on ?x ?y) (clear ?y)) (when(ontable ?y)(clear ?y)) (when(ontable ?x)(not(ontable ?x))) (when (on ?x ?y)(not(on ?x ?y))))) Experimental Result (LAMP)

  13. ?z ?y ?x Experimental Result (HTN-learner) • (:method makestack_from_table_iter :parameters (?x - block ?y - block ?z - block) :task (stack_from_table ?x - block ?y - block) :preconditions (and (ontable ?x) (clear ?z) (holding ?z) (clear ?y) (on ?z ?x)) :subtasks (and (clean ?x ?z) (pick-up ?x) (stack ?x ?y)) • Other methods … • And action models, “pick-up”,…

  14. Related works • Action model learning • Benson, 1995; Wang, 1995; Schmill et al., 2000; Pasula et al., 2007; Walsh and Littman, 2008; Yang et al., 2007; … • Markov Logic Networks (MLNs) • Domingos, 2005; Poon and Domingos, 2007; … • HTN learning • Ilghami et al., 2005; Xu and Mu˜noz-Avila, 2005; Hogg et al., 2008; Yang et al., 2007; …

  15. Conclusion • We have given an overview on several novel approaches to learn applicability conditions in AI Planning, including STRIPS action models, PDDL models with quantifiers and logical implications, and HTN models including action models and method Preconditions. • Our LAMP algorithm enumerates all possible preconditions and effects according to our specific correctness constraints. In the future, we wish to add some form of domain knowledge to further filter out some “impossible” candidate formulas beforehand thereby making the algorithm much more efficient. • We wish to extend the action model learning algorithm to more elaborate action models that explicitly represent resources and functions. • We will also apply our algorithms to more challenging tasks in real world planning applications.

  16. Thank You!

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