Business Processes Modelling MPB (6 cfu, 295AA)
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1 Business Processes Modelling MPB (6 cfu, 295AA) Roberto Bruni Introduction to nets!1
2 Object Overview of the basic concepts of Petri nets Free Choice Nets (book, optional reading) 2
3 Why Petri nets? Business process analysis: validation: testing correctness verification: proving correctness performance: planning and optimization Use of Petri nets (or alike) visual + formal tool supported 3
4 Approaching Petri nets Are you familiar with automata / transition systems? They are fine for sequential protocols / systems but do not capture concurrent behaviour directly A Petri net is a mathematical model of a parallel and concurrent system in the same way that a finite automaton is a mathematical model of a sequential system 4
5 Approaching Petri nets Petri net theory can be studied at several level of details We study some basics aspects, relevant to the analysis of business processes Petri nets have a faithful and convenient graphical representation, that we introduce and motivate next 5
6 Finite automata examples 6
7 Applications Finite automata are widely used, e.g., in protocol analysis, text parsing, video game character behavior, security analysis, CPU control units, natural language processing, speech recognition, mechanical devices (like elevators, vending machines, traffic lights) and many more 7
8 How to define an automaton 1. Identify the admissible states of the system (Optional: mark some states as error states) 2. Add transitions to move from one state to another (no transition to recover from error states) 3. Set the initial state 4. (Optional: mark some states as final states) 8
9 Example: Turnstile 9
10 Example: Turnstile locked 9
11 Example: Turnstile locked unlocked 9
12 Example: Turnstile push locked unlocked 9
13 Example: Turnstile push locked push unlocked 9
14 Example: Turnstile push locked card push unlocked 9
15 Example: Turnstile push locked card unlocked push card 9
16 Example: Turnstile push locked card unlocked push card 9
17 Example: Vending Machine start $ ($1.25 per soda)
18 Example: Vending Machine $0.25 start $0.00 $ ($1.25 per soda)
19 Example: Vending Machine $0.25 $0.25 start $0.00 $0.25 $ ($1.25 per soda)
20 Example: Vending Machine $0.25 $0.25 $0.25 start $0.00 $0.25 $0.50 $ ($1.25 per soda)
21 Example: Vending Machine $0.25 $0.25 $0.25 start $0.00 $0.25 $0.50 $0.75 $0.25 $ ($1.25 per soda)
22 Example: Vending Machine $0.25 $0.25 $0.25 start $0.00 $0.25 $0.50 $0.75 $0.25 $0.25 $1.00 $ ($1.25 per soda)
23 Example: Vending Machine $0.25 $0.25 $0.25 start $0.00 $0.25 $0.50 $0.75 $1.00 $1.00 $1.00 $1.00 $0.25 $0.25 $1.00 $1.25 $1.50 $1.75 $1.00 $ ($1.25 per soda)
24 Example: Vending Machine $0.25 $0.25 $0.25 start $0.00 $0.25 $0.50 $0.75 $1.00 $1.00 $1.00 $1.00 $0.25 $0.25 $1.00 $1.25 $1.50 $1.75 $1.00 $0.25, $1.00 $0.25, $1.00 $0.25, $1.00 $2.00 $0.25, $ ($1.25 per soda)
25 Example: Vending Machine $0.25 $0.25 $0.25 start $0.00 $0.25 $0.50 $0.75 $1.00 $1.00 $1.00 $1.00 $0.25 select select select $0.25 $1.00 $1.25 $1.50 $1.75 $1.00 select $0.25, $1.00 $0.25, $1.00 $0.25, $1.00 $2.00 $0.25, $ ($1.25 per soda)
26 Example: Vending Machine select select select $0.25 $0.25 $0.25 select start $0.00 $0.25 $0.50 $0.75 $1.00 $1.00 $1.00 $1.00 $0.25 select select select select $0.25 $1.00 $1.25 $1.50 $1.75 $1.00 select $0.25, $1.00 $0.25, $1.00 $0.25, $1.00 $2.00 $0.25, $ ($1.25 per soda)
27 Computer controlled characters for games States = characters behaviours Transitions = events that cause a change in behaviour Example: Pac-man moves in a maze wants to eat pills is chased by ghosts by eating power pills, pac-man can defeat ghosts 11
28 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 12
29 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 13
30 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 14
31 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 15
32 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 16
33 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 17
34 Example: Pac-Man Ghosts start Wander the Maze Reach Central Base Power Pellet Expires Spot Pac-Man Lose Pac-Man Pac-Man Eats Power Pellet Chase Pac-Man Pac-Man Eats Power Pellet Return to Base Eaten by Pac-Man Flee Pac-Man 18
35 Exercises Without adding states, draw the automata for a SuperGhost that can t be eaten. It chases Pac-Man when the power pill is eaten, and it returns to base if Pac-Man eats a piece of fruit. Choose a favourite (video) game, and try drawing the state automata for one of the computer controlled characters in that game. 19
36 From automata to Petri nets 20
37 Some basis Are you familiar with the following concepts? Set notation ; a 2 A A B A B }(A) Functions f : A! B Predicate logic tt P ^ Q P _ Q P P! Q 9x.P (x) 8x.P (x) Induction principle (base cases + inductive cases) ( P (0) ^ 8n.( P (n) ) P (succ(n)) ) ) ) 8n.P (n) 24
38 Kleene-star notation A* Given a set A we denote by A the set of finite sequences of elements in A, i.e.: A = { a 1 a n n 0 ^ a 1,...,a n 2 A } We denote the empty sequence by 2 A For example: A = { a, b } A = {,a,b,aa,ab,ba,bb,aaa,aab,...} 25
39 Inductive definitions A natural number is either: or the successor n+1 of a natural number n A sequence over the alphabet A is either: - the empty sequence ε - or the juxtaposition wa of a sequence w with an element a of A 26
40 Recursively defined functions Let us define the exponential function base case: for any k>0 we set exp(k,0) = 1 inductive case: for any k>0, n 0 we set exp(k,n+1) = exp(k,n) x k 27
41 Recursively defined functions Let us define the exponential function base case: for any k>0 we set exp(k,0) = 1 inductive case: for any k>0, n 0 we set exp(k,n+1) = exp(k,n) x k 28
42 Recursively defined functions Let us define the exponential function base case: for any k>0 we set exp(k,0) = 1 inductive case: for any k>0, n 0 we set exp(k,n+1) = exp(k,n) x k 29
43 DFA A Deterministic Finite Automaton (DFA) isatuplea =(Q,,,q 0,F), where Q is a finite set of states; is a finite set of input symbols; : Q! Q is the transition function; q 0 2 Q is the initial state (also called start state); F Q is the set of final states (also accepting states) 30
44 Extended transition function (destination function) Given A =(Q,,,q 0,F), wedefine b : Q! Q by induction: base case: For any q 2 Q we let b (q, ) =q inductive case: For any q 2 Q, a 2,w 2 we let b (q, wa) = ( b (q, w),a) ( b (q, w) returns the state reached from q by observing w) 31
45 Extended transition function (destination function) Given A =(Q,,,q 0,F), wedefine b : Q! Q by induction: base case: For any q 2 Q we let b (q, ) =q inductive case: For any q 2 Q, a 2,w 2 we let b (q, wa) = ( b (q, w),a) ( b (q, w) returns the state reached from q by observing w) 32
46 Extended transition function (destination function) Given A =(Q,,,q 0,F), wedefine b : Q! Q by induction: base case: For any q 2 Q we let b (q, ) =q inductive case: For any q 2 Q, a 2,w 2 we let b (q, wa) = ( b (q, w),a) ( b (q, w) returns the state reached from q by observing w) 33
47 String processing Given A =(Q,,,q 0,F) and w 2 we say that A accept w i b (q0,w) 2 F The language of A =(Q,,,q 0,F) is L(A) ={ w b (q0,w) 2 F } 34
48 Transition diagram We represent A =(Q,,,q 0,F) as a graph s.t. Q is the set of nodes; { q a! q 0 q 0 = (q, a) } is the set of arcs. Plus some graphical conventions: there is one special arrow Start with Start! q 0 nodes in F are marked by double circles; nodes in Q \ F are marked by single circles. 35
49 String processing as paths A DFA accepts a string w, if there is a path in its transition diagram such that: it starts from the initial state it ends in one final state the sequence of labels in the path is exactly w 36
50 DFA: example Start 0 1 q 0 q 1 q ,
51 DFA: example Start 0 1 q 0 q 1 q , 1 q
52 DFA: example Start 0 1 q 0 q 1 q , 1 q 0 1 q
53 DFA: example Start 0 1 q 0 q 1 q , 1 q 0 1 q 0 1 q
54 DFA: example Start 0 1 q 0 q 1 q , 1 q 0 1 q 0 1 q 0 1 q
55 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q
56 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q
57 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q
58 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F
59 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q
60 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q
61 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q 0 0 q
62 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q 0 0 q 1 0 q
63 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q 0 0 q 1 0 q 1 1 q
64 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q 0 0 q 1 0 q 1 1 q 2 1 q
65 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q 0 0 q 1 0 q 1 1 q 2 1 q 2 0 q 2 37
66 DFA: example Start q q 1 q , 1 q 0 1 q 0 1 q 0 1 q 0 0 q 1 0 q 1 0 q 1 2 F q 0 1 q 0 0 q 1 0 q 1 1 q 2 1 q 2 0 q 2 2 F 37
67 DFA: question time Start 0 1 q 0 q 1 q , 1 Does it accept 100? Does it accept 011? Does it accept ? What is L(A)? 38
68 Transition table Conventional tabular representation its rows are in correspondence with states its columns are in correspondence with input symbols its entries are the states reached after the transition Plus some decoration start state decorated with an arrow all final states decorated with * 43
69 Transition table a! q (q, a) 44
70 Start DFA: example 0 1 q 0 q 1 q , 1 0 1! q 0 q 1 q 0 q 1 q 1 q 2? q 2 q 2 q 2 45
71 Start DFA: example 0 1 q 0 q 1 q , 1 0 1! q 0 q 1 q 0 q 1 q 1 q 2? q 2 q 2 q 2 45
72 DFA: exercise Does it accept 100? Does it accept 1010? Write its transition table. What is L(A)? 46
73 NFA A Non-deterministic Finite Automaton (NFA)isatupleA =(Q,,,q 0,F), where Q is a finite set of states; is a finite set of input symbols; powerset of Q = set of sets over Q : Q! }(Q) is the transition function; q 0 2 Q is the initial state (also called start state); F Q is the set of final states (also accepting states) 47
74 NFA: example Start 0 1 q 0 q 1 q 2 0, 1 Can you explain why it is not a DFA? 48
75 Reshaping 50
76 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
77 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
78 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
79 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
80 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
81 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
82 Step 1: get a token Start 0 1 q 0 q 1 q 2 0,
83 Step 2: forget initial state decoration 0 1 q 0 q 1 q 2 0, 1 52
84 Step 3: transitions as 1 boxes q 0 0 q 1 1 q
85 Step 4: forget final states 1 q 0 0 q 1 1 q
86 Step 5: allow for more 1 tokens q 0 0 q 1 1 q
87 Example: token game 1 q 0 0 q 1 1 q
88 Example: token game 1 q 0 0 q 1 1 q
89 Example: token game 1 q 0 0 q 1 1 q
90 Example: token game 1 q 0 0 q 1 1 q
91 Example: token game 1 q 0 0 q 1 1 q
92 Example: token game 1 q 0 0 q 1 1 q
93 Step 6: allow for more 1 arcs q 0 0 q 1 1 q
94 Terminology 0 Arc Token Transition Place 58
95 Example: token game 1 q 0 0 q 1 1 q
96 Example: token game 1 q 0 0 q 1 1 q
97 Example: token game 1 q 0 0 q 1 1 q
98 Example: token game 1 q 0 0 q 1 1 q
99 Example: token game 1 q 0 0 q 1 1 q
100 Example: token game 1 q 0 0 q 1 1 q
101 Example: token game 1 q 0 0 q 1 1 q
102 Example: token game 1 q 0 0 q 1 1 q
103 Some hints Nets are bipartite graphs: arcs never connect two places arcs never connect two transitions Static structure for dynamic systems: places, transitions, arcs do not change tokens move around places Places are passive components Transitions are active components: tokens do not flow! (they are removed or freshly created) 64
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