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

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