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Understanding Planning Agents in Artificial Intelligence: Concepts and Techniques

This chapter explores the role of planning agents in artificial intelligence, detailing their differences from problem-solving agents. It covers the representation of goals, states, and actions, and provides techniques for searching for solutions. Key topics include the structure of simple planning agents, the shift from problem-solving to planning, and practical applications in situations such as shopping problems. The importance of basic representations, including STRIPS, for effective planning is also discussed, along with operators, plans, and the search spaces involved in automated planning processes.

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Understanding Planning Agents in Artificial Intelligence: Concepts and Techniques

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  1. Chap.11 Planning CS570 Artificial Intelligence Kwang-hyung Lee

  2. Planning AgentSimilar to a problem-solving agent construct plans that achieve its goal execute them • Difference from a problem-solving agent • representation of goals, states, actions • ways to search for solutions

  3. 11.1 Simple planning agent 11.1 A simple planning agent • An agent uses percepts to build a complete and correct model • It calls a planning algorithm to generate a plan of action • The agent executes the steps of the plan • planning agent in Figure 11.1problem-solving agent in Figure 3.1

  4. 11.1 Simple planning agent

  5. 11.1 Simple planning agent

  6. 11.2 Form problem solving to planning 11.2 From problem Solving to planning • Difference • representations of goals states, actions • representation and construction of action sequences • Basic element of problem-solving • representation of actions • representation of states • representation of goals • representation of plans

  7. 11.2 Form problem solving to planning • Example:Shopping problem“Get a quart of milk, a bunch of banana and a variable-speed cordless drill” • need to define • initial state • operations

  8. 11.2 Form problem solving to planning

  9. 11.2 Form problem solving to planning • Problem solver in Figure 11.2 • too many branches • too many actions, states • heuristic evaluation function • Problem-Solving agent • consider sequence of actions from the initial state • decide what to do in the initial state when relevant choices are • It cannot decide where to go until the agent figures out how to obtain items

  10. 11.2 Form problem solving to planning • Planning • “Open up” the representation of states, goals and actions states and goals : sets of sentences actions : logical description of precondition and effects direct connections between states and actionsex) goal : conjunctionHave(Milk)  Buy(x) • “free” to add actions • most goals of the world are independent of most other partsdivide-and-conquer strategy

  11. 11.3 Planning in Situation Calculus 11.3 Planning in Situation Calculus • A planning problem represented in situation calculus by logical sentences • initial state : For shopping problemAt(Home,s0)  ¬Have(Milk, s0)  ¬Have(Banana, s0)  ¬Have(Drill,s) • goal state: a logical querys At(Home,s)  Have(Milk,s)  Have(Bananas,s)  Have(Drill,s)

  12. 11.3 Planning in Situation Calculus • Operators : description of actionsa,s Have(Milk,Result(a,s))  [(a=Buy(Milk)  At(Supermarket,s)  (Have(Milk,s)  a  Drop(Milk))]Result’(l,s) means result from sequence of actions l starting in s.s Result’([],s)=sa,p,s Result’([a|p],s)=Result’(p,Result(a,s))

  13. 11.3 Planning in Situation Calculus • A solution to the shopping problem is a plan P, applied to So, yields a situation satisfying the goal query :At(Home, Result’(p,s0))  Have(Milk,Result’(p, s0))  Have(Bananas, Result’(p, s0)  Have(Drill,Result’ (p, s0))P=[Go(Supermarket),Buy(Bananas), Go(HardwareStore),Buy(Drill),Go(Home)] • To make planning practical(1) Restrict the language(2) use a special-purpose algorithm

  14. 11.4 Basic Representations for planning 11.4 Basic Representations for planning • STRIPS: Representation for state and goals • initial state for the shopping problemAt(Home)  ¬Have(Milk)  ¬Have(Banana)  ¬Have(Drill)  ……incomplete state description • goalAt(Home)  Have(Milk)  Have(Banana)  Have(Drill) It can contain variablesAt(x)  Sells(x,Milk) • The initial and goal state are input to planning systems

  15. 11.4 Basic Representations for planning • Representation for actions • STRIP operations consist of • action description • precondition • effect • ex)Op(ACTIOIN:Go(there), PRECOND:At(here)  Path(here,there), EFFECT:At(there) ¬At(here)) • operator schema: an operator with variables • an operator o is applicable in a state s if  way to instantiate the variables in o, so that every one of the precondition of o is true in s.That is, if Precond(o)  s

  16. 11.4 Basic Representations for planning

  17. 11.4 Basic Representations for planning • Situation space and plan space • Situation space • progression planner : forward search • regression planner : backward search • partial plan : expand it with a complete plan that solves the problem  plan space • Refinement operators take a partial plan and add constraints to it • Search through the space of situations Search through the space of plans

  18. 11.4 Basic Representations for planning • Representation for plans • To search through a space of plans • ex) “putting on a pair of shoes” • goal : RightShoeOn  LeftShoeOn • initial state : no literal • operators:Op(ACTION:RightShoe,PRECOND:RightSockOn,EFFECT:RrightShoeOn)Op(ACTION:RightSock,EFFECT:RrightSockOn)Op(ACTION:LeftShoe,PRECOND:LeftSockOn,EFFECT:LeftShoeOn)Op(ACTION:LeftSock,EFFECT:LeftSockOn) • partial plan : RightShoe, LeftShoe • least commitment - partial ordertotal order - linearization

  19. 11.4 Basic Representations for planning • A plan is defined as a data structure • A set of plan steps • A set of step ordering • A set of variable binding constraints • A set of causal links : sic sj”si achieves c for sj” • initial plan before any refinementsStart < FinishRefine and manipulate until a plan that is a solution

  20. 11.4 Basic Representations for planning - Initial plan

  21. 11.4 Basic Representations for planning

  22. 11.4 Basic Representations for planning

  23. 11.4 Basic Representations for planning • Solutions • solution : a plan that an agent guarantees achievement of the goal • a solution is a complete and consistent plan • a complete plan : every precondition of every step is achieved by some other step • a consistent plan : no contradictions in the ordering or binding constraints. When we meet a inconsistent plan we backtrack and try another branch

  24. 11.5 A partial-order planning example 11.5 A partial-order planning example • Shopping problem: “get milk, banana, drill and bring them back home” • assumption1)Go action can travel the two locations2)no need money • initial state : operator startOp(ACTION:Start,EFFECT:At(Home)  Sells(HWS,Drill)  Sells(SM,Milk), Sells(SM,Banana)) • goal state : Finish Op(ACTION:Finish, PRECOND:Have(Drill)  Have(Milk)  Have(Banana)  At(Home)) • actions:Op(ACTION:Go(there),PRECOND:At(here),EFFECT:At(there) ¬At(here))Op(ACTION:Buy(x),PRECOND:At(store)  Sells(store,x),EFFECT:Have(x))

  25. 11.5 A partial-order planning example • There are many possible ways in which the initial plan elaborated • one choice : three Buy actions for three preconditions of Finish action • second choice:sells precondition of Buy• Bold arrows:causal links, protection of precondition• Light arrows:ordering constraints

  26. 11.5 A partial-order planning example

  27. 11.5 A partial-order planning example

  28. 11.5 A partial-order planning example • causal links : protected linksa causal link is protected by ensuring that threats are ordered to come before or after the protected link • demotion : placed beforepromotion : placed after

  29. 11.5 A partial-order planning example

  30. 11.5 A partial-order planning example

  31. 11.6 Partial-order planning algorithm 11.6 Partial-order planning algorithm

  32. 11.7 planning with partially instantiated operators 11.7 planning with partially instantiated operators • Variable binding constraints • resolve now with an equality constraint • resolve now with an inequality constraint • resolve later (see fig 11.14)

  33. 11.7 planning with partially instantiated operators

  34. 11.8 knowledge engineering for planning 11.8 knowledge engineering for planning • Methodology for solving problems with the planning approach(1) Decide what to talk about(2) Decide on a vocabulary of conditions, operators, and objects(3) Encode operators for the domain(4) Encode a description of the specific problem instance(5) pose problems to the planner and get back plans

  35. 11.8 knowledge engineering for planning • (ex) The blocks world (1) what to talk about • cubic blocks sitting on a table • one block on top of another • A robot arm pick up a block and moves it to another position (2) Vocabulary • objects:blocks and table • On(b,x) : b is on x • Move(b,x,y) : move b form x to y • ¬exist x On(x,b) or x ¬On(x,b) : precondition • clear(x)

  36. 11.8 knowledge engineering for planning (3)OperatorsOp(ACTION:Move(b,x,y), PRECOND:On(b,x)  Clear(b)  Clear(y), EFFECT:On(b,y)  Clear(x) ¬On(b,x) ¬Clear(y))Op(ACTION:MoveToTable(b,x), PRECOND:On(b,x)  Clear(b), EFFECT:On(b,Table)  Clear(x) ¬On(b,x))

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