Lecture 3-1CS251: Intro to AI/Lisp II Planning to Learn, Learning to Plan

Lecture 3-1CS251: Intro to AI/Lisp II Announcements Quiz A Review of AI Planning Techniques Reading for next time: Cognitive model for planning Allegro for Windows Project deadline I

Lecture 3-1CS251: Intro to AI/Lisp II What is planning? “Figuring out what to do next” Wumpus agent already does that with: –First-order logic –Resolution Shortcomings –Default values –Efficiency

Lecture 3-1CS251: Intro to AI/Lisp II Why do we need planners?

Lecture 3-1CS251: Intro to AI/Lisp II STRIPS Planning State space search –Just like the search we saw last quarter “It’s all in the operators” What does a STRIPS operator look like?

Lecture 3-1CS251: Intro to AI/Lisp II STRIPS Operators Go(there) At(here), Path(here, there) At(there),  At(here)

Lecture 3-1CS251: Intro to AI/Lisp II Planning Terminology I STRIPS ops –Action description –Precondition –Effect / Postconditions / Add & Delete Operator schemata When is operator o applicable in situation s?

Lecture 3-1CS251: Intro to AI/Lisp II Planning Terminology II The final frontier of planning: space –State (situation) space –Plan space Plan space is populated by __________ Operators –Refine by eliminating plans from the set of plans under consideration –Modify plans by messing with them

Lecture 3-1CS251: Intro to AI/Lisp II Planning in Plan Space NOAH planner (Sacerdoti 1975) was first partial-order planner In state space, solution is a path –Series of operators In plan space, series of plan transformations –Examples: Expand detail, adding ordering constraints

Lecture 3-1CS251: Intro to AI/Lisp II Pruning the Search Space Cutting down the search space –Means-end analysis –Prioritize goals –Identify interactions –Parallelism Abstraction levels –Different approaches –Early: NOAH, ABSTRIPS (Sacerdoti 1973)

Lecture 3-1CS251: Intro to AI/Lisp II A Problem in Plan Space The goal: Getting milk, banana and a drill and heading home Actions: –Go: From here to there –Buy: We’ve got money Good things to know –Hardware stores sell drills –Supermarkets sell milk and bananas

Lecture 3-1CS251: Intro to AI/Lisp II Getting Started Start with an initial plan Start Finish At(Home) Sells(SM, Banana) Sells(SM, Milk) Sells(HWS, Drill) Have(Milk) Have(Banana) Have(Drill) At(Home)

Lecture 3-1CS251: Intro to AI/Lisp II Next Step Start Finish At(s) Sells(s, Drill) At(s) Sells(s, Milk) Have(Drill) Have(Milk) At(Home) Have(Banana) Buy(Drill) Buy(Milk) Buy(Bananas) At(s) Sells(s, Bananas)

Lecture 3-1CS251: Intro to AI/Lisp II What have we got? Protection –Need to have drill –Buy drill achieves Have(Drill) –If we mess with drill buying, then … –When doesn’t it matter?

Lecture 3-1CS251: Intro to AI/Lisp II And after that... Start Finish At(HWS) Sells(HWS, Drill) At(SM) Sells(SM, Milk) Have(Drill) Have(Milk) At(Home) Have(Banana) Buy(Drill) Buy(Milk) Buy(Bananas) At(SM) Sells(SM, Bananas)

Lecture 3-1CS251: Intro to AI/Lisp II What’s the problem? Need to: –Go from home to hardware store –Go from home to supermarket Pick one and then...

Lecture 3-1CS251: Intro to AI/Lisp II Interactive Problems Big red arrows are protected links Protected from … threats Change the ordering The problem in the abstract –Suppose S 1 achieves c for S 2 –Now S 3 comes along and clobbers c

Lecture 3-1CS251: Intro to AI/Lisp II Project topics Planning –Build a planner from scratch: (AB)STRIPS, NOAH –Explore current planners Robotics –Investigate reactive planning: write a series of RAPs –“Build” a robot using subsumption

Lecture 3-1CS251: Intro to AI/Lisp II Project Topics II Perception –Explore audio perception –Write an object recognizer (Pepsi cans in Wayne’s World) Machine learning –Look at data mining (name & from newsgroup sigs)

Lecture 3-1CS251: Intro to AI/Lisp II Project Topics III Uncertainty –Build a system that constructs Bayesian networks –Look at HMMs in speech recognition Natural language –Write a story generator –Tell jokes