STATE MACHINES IN ARTIFICIAL INTELLIGENCE

Recommendations

Info

destination vertex. We have these connections : A → B with multiplicity 3 in graph and subgraph A → D with multiplicity 1 in graph and subgraph A → E with multiplicity 1 in graph and subgraph If a multiplicity of subgraph is greater of a multiplicity of a graph the algorithm terminates with fault. If this does happen, with must associate the combinations of vertices of graph and subgraph. In this example we have: A → B [2=11; 3=12; 4=13] [2=11; 3=13; 4=12] [2=12; 3=11; 4=13] [2=12; 3=13; 4=11] [2=13; 3=11; 4=12] [2=13; 3=12; 4=11] A → D [6=15] A → E [7=16] The final hypothesis are: [2=11; 3=12; 4=13; 6=15;7=16] [2=11; 3=13; 4=12; 6=15;7=16] [2=12; 3=11; 4=13; 6=15;7=16] [2=12; 3=13; 4=11; 6=15;7=16] [2=13; 3=11; 4=12; 6=15;7=16] [2=13; 3=12; 4=11; 6=15;7=16] 14
At the next step we repeat the previous step looking for those vertices not present in the hypothesis. In this case we have only one connection: B → C For the first hypothesis we have: [2=11; 3=12; 4=13; 6=15;7=16] [5=14] for the second hypothesis we have : [2=11; 3=13; 4=12; 6=15;7=16] [5=14] the other hypothesis has a fault: [2=12; 3=11; 4=13; 6=15;7=16] [ 5=/ ] in fact if we associate the vertex 3 to vertex 11 there are no desired connections. Finally we have: [2=11; 3=12; 4=13; 6=15;7=16] [5=14] [2=11; 3=13; 4=12; 6=15;7=16] [5=14] [2=12; 3=11; 4=13; 6=15;7=16] [5=/] [2=12; 3=13; 4=11; 6=15;7=16] [5=/] [2=13; 3=11; 4=12; 6=15;7=16] [5=/] [2=13; 3=12; 4=11; 6=15;7=16] [5=/] At this point we have no vertex without hypothesis and we can perform the matching step within the matrix where the identifier has changed according to the hypothesis H 1 : [2=11; 3=12; 4=13; 6=15;7=16;5=14]. 15
Page 1: Giovanni Alestra STATE MACHINES IN
Page 5 and 6: Contents Introduction 7 1.1 Definit
Page 7 and 8: INTRODUCTION The aim of this book i
Page 9 and 10: position (i,i) we have a one, and t
Page 11 and 12: We can model this situation through
Page 13: also if the concept is simple, find
Page 17 and 18: 1.7 Sliding connections A sliding c
Page 19 and 20: information of the sliding connecti
Page 21 and 22: Suppose we want to go from Trapani
Page 23 and 24: 13. Return “From “ + StartPlace
Page 25 and 26: y transcription, semantic informatu
Page 27 and 28: In this way, when we have an assert
Page 29 and 30: This problem can be solved by the d
Page 31 and 32: 5. Print “The Set “; String ( C
Page 33 and 34: idenfier and values. Finally we app
Page 35 and 36: The counting is a graph programming
Page 37 and 38: Suppose, for example, we have a che
Page 39 and 40: m Hs m /∏ i=1 Ti −1 We can set
Page 41 and 42: 41 The existence of the desired tra
Page 43 and 44: We have a connection between two re
Page 45 and 46: 4.5 Identity Transformation The ide
Page 47 and 48: 4.7 Deletion of zero-record When we
Page 49 and 50: PART III LOGICAL INFERENCE CHAPTER
Page 51 and 52: Possible Move of Rook: X r t1= X r
Page 53 and 54: X r t X d ∗a= X k t Y r t Y d
Page 55 and 56: X r t − X k t Y r t −Y k t -1 0
Page 57 and 58: 5.4 Structured Pieces A rook can ha
Page 59 and 60: if X rt=−1 X k1t∧Y rt−Y k1t=
Page 61 and 62: The capture is possible when : we b
Page 63 and 64: PART IV STATE ANALYSIS CHAPTER 6 :
Page 65 and 66:
not d5=0∧d6=0 not d5=d6∧d7=d8 I
Page 67 and 68:
SW i=[1,0 ,0,0 ,0,1 ,0,0 ,1,0 ,...
Page 69 and 70:
… We can write the formulas we ha
Page 71 and 72:
Therefore we know how the system wo
Page 73 and 74:
PART V HUMAN KNOWLEDGE CHAPTER 7 :
Page 75:
CONCLUSIONS At the beginnig of the
show all

STATE MACHINES IN ARTIFICIAL INTELLIGENCE

Create successful ePaper yourself

Delete template?

Save as template?