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CS 221: Artificial Intelligence Planning (and Basic Logic) Peter Norvig and Sebastian Thrun Slide credits: Stuart Russell, Rina Dechter,  Rao Kambhampati
AI: Dealing with Complexity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Finding Actions
What’s wrong with Problem-Solving Plan: [Forward, Forward, Forward, …]
What’s wrong with Problem-Solving
Planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dealing with Partial Observability: World vs. Belief States
Sensorless – Belief States - Conformant Plans
Deterministic world Slippery wheels  Partial (local) Observability and Stochastic Worlds Deterministic actions; observe only local square Observe only  local square; Suck  is determ. R/L  are stoch. (may fail to move)
Slippery wheels  Planning and Sensing in  Partially Observable and Stochastic World What is a plan to achieve all states clean? [1: Suck ; 2: Right ; ( if  A :  goto  2); 3: Suck ] also written as [ Suck ; ( while  A :  Right );  Suck ] Observe only  local square; Suck  is determ. R/L  are stoch.; may fail to move
Search Graph as And/Or Tree What do we need to  guarantee success? What kind of guarantee?
As Equations, not Tree ,[object Object],[object Object],[object Object],[object Object]
Kindergarten world: dirt may appear anywhere at any time, But actions are guaranteed to work. b1  b3 =UPDATE( b1 ,[ A,Clean ])  b5  = UPDATE( b4,… )  b2 =PREDICT( b1, Suck )  b4 =PREDICT( b3 , Right )
State Representation
State Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Planning with Factored States ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Classical” Planning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Expressiveness of the language
Advantages of the Language ,[object Object],[object Object],[object Object],[object Object],[object Object]
Planning Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Plan-space search
Plan-space search Start Finish Left  Sock Finish Start Right Shoe Finish Start Right Shoe Finish Start Left Shoe
Plan-space search
Progression vs. Regression ,[object Object],[object Object],[object Object],[object Object],[object Object],You can also do bidirectional search stop when a (leaf) state in the progression tree entails a (leaf) state (formula) in the regression tree A B A B
State of the art ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
8-puzzle state space
8-puzzle action schema ,[object Object]
8-puzzle heuristics
Convex search: ignore del lists
Factored Rep allows control
Factored Rep allows control
Beyond Classical Planning ,[object Object],[object Object],[object Object],[object Object],[object Object]
First-Order Logic ,[object Object],[object Object],[object Object],[object Object],[object Object]
Situation Calculus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Situation Calculus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Situations as Result of Action Situation Calculus First-order Logic ∃s, p : Goal(s) ∧ s = Result(s0, p) s = Result(s, []) Result(s, [a,b,…]) = Result(Result(s, a), [b,…])
Planning Graphs ,[object Object],[object Object],[object Object],[object Object],[object Object]
Planning Graphs ,[object Object],[object Object],[object Object],[object Object],[object Object]
Planning Graphs ,[object Object],[object Object],[object Object]
Planning Graph Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Planning Graph Example Create level 0 from initial problem state.
Planning Graph Example Add all applicable actions. Add all effects to the next state.
Planning Graph Example Add  persistence actions  (inaction = no-ops)  to map all literals in state S i  to state S i+1 .
Planning Graph Example Identify  mutual exclusions  between actions and literals based on potential conflicts.
Mutual exclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cake example ,[object Object],[object Object],[object Object]
Cake example ,[object Object],[object Object]
PG and Heuristic Estimation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PG and Heuristic Estimation ,[object Object],[object Object],[object Object],[object Object]
The GRAPHPLAN Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GRAPHPLAN example ,[object Object],[object Object],[object Object],[object Object],[object Object]
GRAPHPLAN example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GRAPHPLAN example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GRAPHPLAN Termination ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Cs221 lecture7-fall11

Notas do Editor

  1. This course: Spiral approach to dealing with complexity
  2. Executing a plan without looking at the world often fails
  3. In the real world actions are at many levels: the action of driving from one city to the next involves a series of intersection-to-intersection actions, each of which involves a sequence of steering wheel, accelerator, and brake actions.
  4. Deterministic, fully observable world: how to get from upper left to clean state?
  5. Conformant plan to clean everything? Didn’t know where we were, but didn’t matter
  6. Slippery wheels: may move, may not. Suck always works.
  7. Slippery wheels: may move, may not. Suck always works.
  8. Does predict add or subtract states? Does update?
  9. What about h(s)? Is that atomic?
  10. In classical planning, schema over finite domain – abbreviation for spelling out individual variables.