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F-planning

# F-planning - (Where logic-based representation of knowledge...

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1 1 Action Planning (Where logic-based representation of knowledge makes search problems more interesting) R&N: Chap. 11, Sect. 11.1–4 2 The goal of action planning is to choose actions and ordering relations among these actions to achieve specified goals Search-based problem solving applied to 8-puzzle was one example of planning, but our description of this problem used specific data structures and functions Here, we will develop a non-specific , logic-based language to represent knowledge about actions, states, and goals, and we will study how search algorithms can exploit this representation 3 Knowledge Representation Tradeoff Expressiveness vs. computational efficiency STRIPS: a simple, still reasonably expressive planning language based on propositional logic 1) Examples of planning problems in STRIPS 2) Planning methods 3) Extensions of STRIPS Like programming, knowledge representation is still an art SHAKEY the robot 4 STRIPS Language through Examples 5 Vacuum-Robot Example Two rooms: R 1 and R 2 A vacuum robot Dust R 1 R 2 6 State Representation Propositions that “hold” (i.e. are true) in the state Logical “and” connective R 1 R 2 In(Robot, R 1 ) Clean(R 1 )

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2 7 State Representation In(Robot, R 1 ) Clean(R 1 ) R 1 R 2 Conjunction of propositions No negated proposition, such as ¬ Clean(R 2 ) Closed-world assumption: Every proposition that is not listed in a state is false in that state No “or” connective, such as In(Robot,R 1 ) In(Robot,R 2 ) No variable, e.g., x Clean(x) 8 Goal Representation A goal G is achieved in a state S if all the propositions in G (called sub-goals ) are also in S Example: Clean(R 1 ) Clean(R 2 ) Conjunction of propositions No negated proposition No “or” connective No variable 9 Action Representation Right Precondition = In(Robot, R 1 ) Delete-list = In(Robot, R 1 ) Add-list = In(Robot, R 2 ) R 1 R 2 R 1 R 2 In(Robot, R 1 ) Clean(R 1 ) In(Robot, R 2 ) Clean(R 1 ) Right 10 Action Representation Right Precondition = In(Robot, R 1 ) Delete-list = In(Robot, R 1 ) Add-list = In(Robot, R 2 ) Same form as a goal: conjunction of propositions Sets of propositions 11 Action Representation An action A is applicable to a state S if the propositions in its precondition are all in S The application of A to S is a new state obtained by deleting the propositions in the delete list from S and adding those in the add list Right Precondition = In(Robot, R 1 ) Delete-list = In(Robot, R 1 ) Add-list = In(Robot, R 2 ) 12 Other Actions Left P = In(Robot, R 2 ) D = In(Robot, R 2 ) A = In(Robot, R 1 ) Suck(R 1 ) P = In(Robot, R 1 ) D = [empty list] A = Clean(R 1 ) Suck(R 2 ) P = In(Robot, R 2 ) D = [empty list] A = Clean(R 2 )
3 13 Other Actions Left P = In(Robot, R 2 ) D = In(Robot, R 2 ) A = In(Robot, R 1 ) Suck(r) P = In(Robot, r) D = [empty list] A = Clean(r) 14 Action Schema Suck(r) P = In(Robot, r) D = A = Clean(r) Parameter that will get “instantiated” by matching the precondition against a state It describes several actions, here: Suck(R 1 ) and Suck(R 2 ) 15 Action Schema Suck(r) P = In(Robot, r) D = A = Clean(r) R 1 R 2 In(Robot, R 2 ) Clean(R 1 ) R 1 R 2 In(Robot, R 2 )

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