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Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
Knowledge Representation & Problem Solving with PDDL
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Knowledge Representation & Problem Solving with PDDL

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  1. Knowledge Representation & Problem Solving with Planning Domain Definition Language (PDDL) Vladimir Kulyukin
  2. Outline       Review of Basic FOPC Concepts PDDL Background PDDL Problem Specification STRIPS Semantics of PDDL Actions Sussman Anomaly Solving Sussman Anomaly with BLACKBOX
  3. Review of Basic Concepts of FOPC
  4. Review: Objects & Relations ● Knowledge formalization begins with a conceptualization of the world ● Conceptualization, generally speaking, analyzes the world in terms of objects and relations ● Functions are also relations ● Objects can be concrete (book, pen, block) or abstract (number 2, honesty, love) ● Objects can be primitive (number 2) or abstract (algebraic expression)
  5. Review: Objects & Relations ● It is impossible for any conceptualization to include all objects in the world ● Conceptualizations reside inside observers (human or mechanical) and include only those objects that present some interest to the observers ● Beekeepers conceptualize the world in terms bees, swarms, beehives, honey extractors, bee disease treatments, etc.; number theorists conceptualize the world in terms of numbers, sets, properties of numbers, etc. ● The set of objects covered by a conceptualization is called the universe of discourse
  6. Review: Functional & Relational Bases ● Once a conceptualization has objects, the observer must establish relations among those objects ● There are two types of relations most conceptualizations contain: functions and relations ● A set of functions in the conceptualization is called functional basis ● A set of relations in the conceptualization is called relational basis
  7. Review: Conceptualizations & Observers ● Conceptualizations, although they are written down, consists of the objects and relations the observer actually sees in the world ● The same world may have multiple conceptualizations (e.g., blocks world can be conceptualized in terms of line segments, curves, and their relations) ● Different conceptualizations allow/inhibit certain kinds of knowledge (light as a wave vs. light as a particle; geocentric vs. heliocentric universe)
  8. Review: FOPC Terms ● A, B, C, D, E are object constants and, therefore, terms ● Hat is a function constant ● Hat(C) is a term (functional expression) ● Hat(Hat(C)) is a term (functional expression) ● Hat(x) is a term (functional expression) ● Hat(Hat(x)) is a term (functional expression)
  9. Review: FOPC Well-Formed Formulas ● In FOPC, facts are stated in sentences (aka well-formed formulas or wffs) ● Three types of sentences: – Atomic sentences (aka atoms) – Logical sentences Quantified sentences –
  10. PDDL Problem Specification
  11. Background PDDL is a standard knowledge representation (KR) language for classical planning problems  PDDL has been the de-facto standard for several International Planning Competitions (IPCs)  PDDL is closely related to other KR formalisms such as ADL (Action Description Language) and APPL (Abstract Plan Preparation Language) and MA-PDDL (Multi Agent PDDL) 
  12. PDDL Components Objects – actual objects in a specific domain  Predicates – statements about objects that are true or false  Initial State – initial state of the world in a specific domain  Goal State – final state of the world that must be reached from the initial state  Actions (aka Operators) – specific actions used to change states of the world 
  13. PDDL Problem Specification Knowledge engineers must do two things to do problem solving describe in PDDL: 1) describe a domain and 2) describe a problem  The description of the domain is placed into a domain file  The description of the problem is placed into a problem file 
  14. Domain Definition (define (domain <DOMAIN NAME>) <REQUIREMENT>* <PREDICATE>* <ACTION>* ) <REQUIREMENT>* is a statement that specifies requirements (e.g., :typing or :equal) <PREDICATE>* is a sequence of predication specifications <ACTION>* is a sequence of action specifications
  15. Problem Definition (define (problem <PROBLEM NAME>) <DOMAIN NAME> <OBJECT STATEMENT> <INITIAL STATE DESCRIPTION> <GOAL DESCRIPTION> ) <DOMAIN NAME> is a statement that references the domain in which the problem must be solved <OBJECT STATEMENT> is a sequence of object constants <INITIAL STATE DESCRIPTION> is a sequence of predicates that describe the initial state of the world <GOAL DESCRIPTION> is a sequence of predicates that describe the goal state of the world
  16. STRIPS Semantics of PDDL Actions STRIPS (Stanford Research Institute Planning System) is an AI Planner developed by Richard Fikes and Nils Nilson in 1971  A STRIPS operator has preconditions (a set of predicates that must be true in the current state of the world for the operator to be considered application) and postconditions (a set of predicates that will be true in the state of the world that results from the operator’s application)  A PDDL action also has preconditions and postconditions called effects 
  17. STRIPS Semantics of PDDL Actions SYMBOLIC VARIABLES THAT BIND TO OBJECT CONSTANTS PREDICATE STATEMENTS THAT MUST BE TRUE IN THE CURRENT STATE OF THE WORLD FOR THE ACTION <ACTION_NAME> TO EXECUTE (:action <ACTION_NAME> :parameters (?x1 ?x2 …. ?xn) :precondition (<PREDICATE STATEMENT>*) :effect (<PREDICATE STATEMENT>*) ) PREDICATE STATEMENTS THAT WILL BE TRUE IN THE WORLD AFTER THE ACTION IS EXECUTED
  18. Sussman Anomaly
  19. Sussman Anomaly: Definition The Sussman Anomaly is an AI planning problem described the MIT computer scientist Gerald Sussman. Suppose our universe of discourse (domain) has the following object constants: A, B, C, and T (this is the table). Suppose the robot must find a plan (sequence of actions) to transform the initial state of the world (on the left) into the goal state (on the right) by moving one block at a time. PLAN Initial state: Goal State: (ON B T) (ON A T) (ON C A) (ON A B) (ON B C) (ON C T)
  20. Sussman Anomaly: Definition Suppose the robot starts working on the sub-goal (ON A B). The robot can put C on T and then put A on B. Thus, (ON A B) is achieved. But now to achieve the sub-goal (ON B C), the robot must un-achieve (ON A B), because B is not clear and cannot be moved on top of C. PUT C ON T; PUT A ON B Initial state: Intermediate State: (ON B T) (ON A T) (ON C A) (ON A B) (ON B T) (ON C T)
  21. Sussman Anomaly: Definition The Sussman Anomaly demonstrates that sequential achievement of goals is not always straightforward because the achievement of one goal may clobber a previously achieved different goal.
  22. STRIPS Semantics of PDDL Actions PREDICATE STATEMENTS THAT MUST BE TRUE IN THE WORLD FOR THE ACTION TO EXECUTE SYMBOLIC CONSTANTS (:action <ACTION_NAME> :parameters (?x1 ?x2 …. ?xn) :precondition (<PREDICATE STATEMENT>*) :effect (<PREDICATE STATEMENT>*) ) PREDICATE STATEMENTS THAT WILL BE TRUE IN THE WORLD AFTER THE ACTION IS EXECUTED
  23. Solving Sussman Anomaly with BLACKBOX
  24. Problem Statement Let us solve the Sussman Anomaly with BLACKBOX, an AI planner developed by Henry Kautz & Bart Selman  BLACKBOX can be downloaded from here  To solve a problem in BLACKBOX we must create two files: the domain file and the problem specification file  The domain file specifies object constants, predicates, and actions  The problem file specifies a problem we want BLACKBOX to solve by finding a plan (i.e., a sequence of actions) 
  25. Sussman Anomaly Domain w/o Types (define (domain sussman-anomaly) (:requirements :strips :equality) (:predicates (block ?x) (on ?x ?y) (clear ?x) (table ?x)) (:action MOVE_01 :parameters (?b1 ?b2 ?t) :precondition (and (block ?b1) (block ?b2) (table ?t) (clear ?b1) (on ?b1 ?b2) (on ?b2 ?t)) :effect (and (not (on ?b1 ?b2)) (on ?b1 ?t) (clear ?b2))) (:action MOVE_02 :parameters (?b1 ?t ?b2) :precondition (and (block ?b1) (block ?b2) (table ?t) (clear ?b1) (clear ?b2) (on ?b1 ?t) (on ?b2 ?t)) :effect (and (on ?b1 ?b2) (not (on ?b1 ?t)) (not (clear ?b2)))) (:action MOVE_03 :parameters (?b1 ?t ?b2 ?b3) :precondition (and (block ?b1) (block ?b2) (block ?b3) (table ?t) (clear ?b1) (clear ?b2) (on ?b1 ?t) (on ?b2 ?b3) (on ?b3 ?t)) :effect (and (not (on ?b1 ?t)) (on ?b1 ?b2) (not (clear ?b2)))) ) source code is in sa_domain.pddl
  26. Sussman Anomaly Domain: MOVE_01 (:action MOVE_01 :parameters (?b1 ?b2 ?t) :precondition (and (block ?b1) (block ?b2) (table ?t) (clear ?b1) (on ?b1 ?b2) (on ?b2 ?t)) :effect (and (not (on ?b1 ?b2)) (on ?b1 ?t) (clear ?b2))) ;;; ?b1 is a block; ;;; ?b2 is a block ;;; ?t is a table ;;; ?b1 is clear precondition ;;; ?b1 is on ?b2 ;;; ?b2 is on ?t ;;; ?b1 is not on ?b2 ;;; ?b1 is on ?t effect ;;; ?b2 is clear
  27. Sussman Anomaly Domain: MOVE_02 (:action MOVE_02 :parameters (?b1 ?t ?b2) :precondition (and (block ?b1) (block ?b2) (table ?t) (clear ?b1) (clear ?b2) (on ?b1 ?t) (on ?b2 ?t)) :effect (and (on ?b1 ?b2) (not (on ?b1 ?t)) (not (clear ?b2)))) ;;; ?b1 is a block; ;;; ?b2 is a block ;;; ?t is a table precondition ;;; ?b1 is clear ;;; ?b2 is clear ;;; ?b1 is on ?t ;;; ?b2 is on ?t ;;; ?b1 is on ?b2 ;;; ?b1 is not on ?t ;;; ?b2 is not clear effect
  28. Sussman Anomaly Domain: MOVE_03 (:action MOVE_03 :parameters (?b1 ?t ?b2 ?b3) :precondition (and (block ?b1) (block ?b2) (block ?b3) (table ?t) (clear ?b1) (clear ?b2) (on ?b1 ?t) (on ?b2 ?b3) (on ?b3 ?t)) :effect (and (not (on ?b1 ?t)) (on ?b1 ?b2) (not (clear ?b2)))) ;;; ?b1 is a block ;;; ?b2 is a block ;;; ?b3 is a block ;;; ?t is a table ;;; ?b1 is clear ;;; ?b2 is clear ;;; ?b1 is on ?t ;;; ?b2 is on ?t ;;; ?b2 is on ?b3 ;;; ?b3 is on ?t ;;; ?b1 is not on ?t ;;; ?b1 is on ?b2 ;;; ?b2 is not clear precondition effect
  29. Sussman Anomaly Problem OBJECT CONSTANTS INITIAL STATE OF THE WORLD GOAL STATE OF THE WORLD (define (problem sa-problem) (:domain sussman-anomaly) (:objects A B C T) (:init (block A) (block B) (block C) (table T) (on B T) (on A T) (on C A) (clear B) (clear C)) (:goal (and (on A B) (on B C)))) source code is in sa_problem.pddl
  30. BLACKBOX Planning System Output vladimir@VladimirT3500:~/programming/planning$ ./blackbox -o Examples/SussmanAnomaly/sa_domain.pddl -f Examples/SussmanAnomaly/sa_problem.pddl -g Examples/SussmanAnomaly/sa_problem_plan.txt blackbox version 43 ---------------------------------------------------Begin plan 1 (move-01 c a t) 2 (move-02 b t c) 3 (move-03 a t b c) End plan ---------------------------------------------------- The output of BLACKBOX 4.3 on Ubuntu 12.04 LTS -o specifies the domain file -f specifies the problem file -g specifies the file where the plan is saved If –g is omitted, the plan (if one is found) is output in the terminal
  31. Sussman Anomaly Domain with Types (define (domain sussman-anomaly-typed) (:requirements :strips :typing) (:types BLOCK TABLE) (:predicates (on ?x - BLOCK ?y - (either BLOCK TABLE)) (clear ?x - BLOCK)) (:action MOVE_01 :parameters (?b1 - BLOCK ?b2 - BLOCK ?t - TABLE) :precondition (and (clear ?b1) (on ?b1 ?b2) (on ?b2 ?t)) :effect (and (not (on ?b1 ?b2)) (on ?b1 ?t) (clear ?b2))) (:action MOVE_02 :parameters (?b1 - BLOCK ?t - TABLE ?b2 - BLOCK) :precondition (and (clear ?b1) (clear ?b2) (on ?b1 ?t) (on ?b2 ?t)) :effect (and (on ?b1 ?b2) (not (on ?b1 ?t)) (not (clear ?b2)))) (:action MOVE_03 :parameters (?b1 - BLOCK ?t - TABLE ?b2 - BLOCK ?b3 - BLOCK) :precondition (and (clear ?b1) (clear ?b2) (on ?b1 ?t) (on ?b2 ?b3) (on ?b3 ?t)) :effect (and (not (on ?b1 ?t)) (on ?b1 ?b2) (not (clear ?b2)))) ) source code is in sa_domain_typed.pddl
  32. Sussman Anomaly Problem with Types (define (problem sussman-problem-typed) (:domain sussman-anomaly-typed) (:objects A - BLOCK B - BLOCK C - BLOCK T - TABLE) (:init (on B T) (on A T) (on C A) (clear B) (clear C)) (:goal (and (on A B) (on B C)))) source code is in sa_problem_typed.pddl
  33. BLACKBOX Planning System Output vladimir@VladimirT3500:~/programming/planning$ ./blackbox -o Examples/SussmanAnomaly/sa_domain_typed.pddl -f Examples/SussmanAnomaly/sa_problem_typed.pddl -g Examples/SussmanAnomaly/sa_problem_typed_plan.txt blackbox version 43 ---------------------------------------------------Begin plan 1 (move-01 c a t) 2 (move-02 b t c) 3 (move-03 a t b c) End plan ---------------------------------------------------- The output of BLACKBOX 4.3 on Ubuntu 12.04 LTS
  34. References & Reading Suggestions Ch 02, M. Genesereth & N. Nilsson. Logical Foundations of AI, Morgan Kaufmann  Fikes, R., and Nilsson, N., "STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving," Artificial Intelligence, 2(3/4):189-208, 1971. 

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