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Use “Search” for Pathfinding FactorySchool Library Hospital Park Newsagent University church Example from Alison Cawsey’s book start finish.

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Presentation on theme: "Use “Search” for Pathfinding FactorySchool Library Hospital Park Newsagent University church Example from Alison Cawsey’s book start finish."— Presentation transcript:

1 Use “Search” for Pathfinding FactorySchool Library Hospital Park Newsagent University church Example from Alison Cawsey’s book start finish

2 Breadth First Search library school hospital factory park newsagent universitychurch Put things on the back of the list “to visit later”

3 Depth First Search library school hospital factory park newsagent universitychurch Put things on the front of the list “to visit later”

4 Breadth vs. Depth Which is better? library school hospital factory park newsagent university church stadium grocers marketbridge fountain

5 Breadth vs. Depth Which is better? library school hospital factory park newsagent market church stadium grocers bridge university

6 Breadth vs. Depth Which is better?  Depends on problem  Breadth usually needs a lot more memory  Remember all the bits you need to expand next  Breadth could be good if  There are many long dead ends,  But one very short successful path  Depth could be good if  There are many successful paths  But all are quite long  Can also combine – set a depth limit

7 What about a big open space?  See demo…  Break it up into squares  Each node has 8 children (Be careful about looping)  That’s an awfully big tree!  Need some clever tricks…  How would a human do it?  Heuristics  Search we did before is called “blind” or “brute force” (not clever)  Heuristic is a clever rule of thumb

8 Hill-climbing with Heuristic FactorySchool Library Hospital Park Newsagent University church start finish Heuristic: how close to goal

9 Hill-climbing with Heuristic Library 9 School 7 Hospital 5 Factory 5 Park 6 Newsagent 0 University 3Church 4 finish Heuristic: how close to goal

10 Hill-climbing with Heuristic FactorySchool Library Hospital Park Newsagent University church start finish Heuristic: how close to goal

11 Hill-climbing with Heuristic Library 9 School 7 Hospital 5 Factory 5 Park 2 Newsagent 4 University 0Church 4 finish Heuristic: how close to goal

12 Hill-climbing with Heuristic Heuristic: how close to goal from Russell and Norvig’s book

13 Best first Search Library 9 School 7 Hospital 5 Factory 5 Park 2 Newsagent 4 University 0Church 4 finish Heuristic: how close to goal  Order the list of nodes “to visit later”  Do best first  But try others later  Very good, e.g. in open space  Doesn’t consider how far we’ve come though  A* - more in practical

14 Remember: General Problem Solving  Problem formulation  Initial situation  Goal situation  Actions that can be done  +cost of action  Constraints  Task:  Find the best sequence of permissible actions that can transform the initial situation into the goal situation

15 Search is an abstract technique…  Jugs problem  Two jugs, 4 litre and 3 litre  Want to get 2 litres in 4 litre jug  Formulate problem  Can represent state as (0,0) or (4,0) or (4,2)…  Actions:  Fill 4 litre ( _, _ )  ( 4, _ )  Fill 3 litre ( _, _ )  ( _, 3 )  Empty 4 litre ( _, _ )  ( 0, _ )  Empty 3 litre ( _, _ )  ( _, 0 )  What else?

16 Search for games: Minimax

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18 Alpha-Beta pruning example Alpha value I get at least this Beta value I get at most this (if I go here)

19 Alpha-Beta pruning example No question about 3 now

20 What about real (hard) games?  So far we searched all the way to the end of the game  Not feasible in chess, branching factor 35  So far we didn’t use heuristics  Do a limited lookahead  Distance to goal? Evaluate the board state  Requires intelligence: pieces, their positions, and stage in game  How much lookahead?  Modern computer?  Alpha beta can give about double  4 moves ≈ human novice  8 moves ≈ human master  12 moves ≈ Deep Blue, Kasparov  Deep Blue had extra tricks to look further on interesting paths  Go  Branching factor ≈ 300… forget it!  Use databases of patterns

21 What is search good for?  Pretty much everything!  Pathfinding, puzzles, general problem solver, games  Scheduling deliveries  Arranging the CS1013 timetable  Diagnostic systems  find a set of malfunctions that explain the symptoms  Speech recognition  find the right sequence of words  Finding templates/models to match a visual scene  Learning is search for a hypothesis  Planning systems  Find a sequence of actions that achieves a given goal  We will look at this next week

22 Defence A big user of AI. "... the deployment of a single logistics support aid called DART during the Desert Shield/Storm Campaign paid back all US government investment in AI/KBS research over a 30 year period." Tate A. Smart Planning. ARPI Proc

23 Search is an abstract technique…  What are we really doing here?  What is the science of abstraction?  Mathematics  Look at problems abstractly  See that they’re the same  Use one technique for many problems

24 Note on Heuristics  Hard to come up with a good heuristic  Often use human intelligence  Is the chess computer smart?  What about TD-Backgammon  Try the Missionaries and Cannibals   Interesting because human heuristics go awry  Computer is not confused  Remember the first law

25 Recap: What have you learned about “Search”  “Blind” or “brute force” techniques  Breadth first  Depth first  Heuristic techniques  Hill-climbing  Best first  A* - more in practical  General problem solving  Game playing  Minimax  Alpha-Beta pruning  Search can apply to many diverse problems  Makes some tasks simple for computers  Heuristics need some intelligence  Musings…  Computer: Some simple tricks can go a long way  Power of computer to store so much and go so fast  Just like life – simple blocks


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