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The Wumpus World! 2012 级 ACM 班 金汶功. Hunt the wumpus!

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Presentation on theme: "The Wumpus World! 2012 级 ACM 班 金汶功. Hunt the wumpus!"— Presentation transcript:

1 The Wumpus World! 2012 级 ACM 班 金汶功

2 Hunt the wumpus!

3 Description Performance measure Environment Actuators Sensors: Stench & Breeze & Glitter & Bump & Scream

4 An Example

5

6 Reasoning via logic

7 Semantics

8 Models

9 Knowledge base Axioms Current States Sensors Actuators Agent Tell Ask Tell Model checking Answer

10 Efficient Model Checking DPLL Early termination Pure symbol heuristic Unit clause heuristic Component analysis …

11 Drawbacks Model checking is NP-complete Knowledge base may tell nothing.

12 Probabilistic Reasoning

13 Full joint probability distribution P(X, Y) = P(X|Y)P(Y) X: {1,2,3,4} -> {0.1,0.2,0.3,0.4} Y: {a,b} -> {0.4, 0.6} P(X = 2, Y = a) = P(X = 2|Y = a)P(Y = a) The probability of all combination of values

14

15 Normalization

16 The Wumpus World Aim: calculate the probability that each of the three squares contains a pit.

17 Full joint distribution

18 How likely is it that [1,3] has a pit?

19 Using independence

20 Simplification

21

22 Finally

23 Bayesian Network

24 Simple Example BurglaryEarthquake Alarm(Bark) John CallsMary Calls P(B).001 P(E).002 BEP(A) Truetrue.95 truefalse.94 falsetrue.29 false.001 BarkP(J) true.90 false.05 BarkP(M) true.70 false.01

25 Specification

26 Conditional Independence

27 Exact Inference

28

29 P1,3known b P3,1P2,2 P1,3P2,2P3,1b True 1 False1 TrueFalseTrue1 False 0 True 1 FalseTrueFalse1 True0 False 0 P(1,3) 0.2 P(known) P(P3,1) 0.2 P(P2,2) 0.2

30 Approximate Inference Markov Chain Monte Carlo Gibbs Sampling Idea: The long-run fraction of time spent in each state is exactly proportional to its posterior probability.

31

32 Reference 6%AF%E7%BD%91%E7%BB%9C 6%AF%E7%BD%91%E7%BB%9C Stuart Russell, Peter Norvig Artificial Intelligence—A Modern Approach 3 rd edition, 2010


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