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Artificial Intelligence for Games Depth limited search Patrick Olivier [email protected]

Artificial Intelligence for Games Depth limited search Patrick Olivier [email protected]

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Page 1: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Artificial Intelligence for Games

Depth limited search

Patrick Olivier

[email protected]

Page 2: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Recap: Breadth-first search

Page 3: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Roadmap of Romania…

Page 4: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Class Exercise – Uniform-Cost• Uniform-cost search

– Cost to get to a node g(n)– Expand the least-cost unexpanded node– Implement with fringe ordered by path cost

• Romanian holiday:– Initial state: Arad– Goal state: Bucharest– Do a uniform cost search by hand

Page 5: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Recap: Depth-first search

Page 6: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Depth-limited search

• depth-first search with depth limit L• nodes at depth L have no successors

Page 7: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Iterative deepening

• search to a depth limit L

• if no solution research to limit L+1

• previous search repeated

Page 8: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Iterative deepening search: L = 0

Page 9: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Iterative deepening search: L = 1

Page 10: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Iterative deepening search: L = 2

Page 11: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Iterative deepening search: L = 3

Page 12: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Iterative deepening search• Number of nodes generated in a depth-limited search to depth

d with branching factor b: – NDLS = b0 + b1 + b2 + … + bd-2 + bd-1 + bd

• Number of nodes generated in an iterative deepening search to depth d with branching factor b: – NIDS = (d+1)b0 + d.b1 + (d-1)b2 + … + 3bd-2 +2bd-1 + 1bd

• For b=10, d=5:– NDLS = 1 + 10 + 100 + 1,000 + 10,000 + 100,000 = 111,111– NIDS = 6 + 50 + 400 + 3,000 + 20,000 + 100,000 = 123,456

• Overhead = (123,456 - 111,111)/111,111 = 11%

Page 13: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Properties of iterative deepening

• Complete? – Yes

• Time: – (d+1)b0 + d b1 + (d-1)b2 + … + bd = O(bd)

• Space: – O(bd)

• Optimal? – If the cost is the same per step

Page 14: Artificial Intelligence for Games Depth limited search Patrick Olivier p.l.olivier@ncl.ac.uk

Summary of uninformed search