Modelling and Analysis of Communicating Systems: Chapter 6
Upcoming SlideShare
Loading in...5
×
 

Modelling and Analysis of Communicating Systems: Chapter 6

on

  • 271 views

 

Statistics

Views

Total Views
271
Views on SlideShare
263
Embed Views
8

Actions

Likes
0
Downloads
1
Comments
0

1 Embed 8

http://ocw.tudelft.nl 8

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Modelling and Analysis of Communicating Systems: Chapter 6 Modelling and Analysis of Communicating Systems: Chapter 6 Document Transcript

  • Chapter 6The modal µ-calculusIn this chapter we discuss how to denote properties of a reactive system. A property describes some aspectof the behaviour of a system. For instance, deadlock freedom is a simple, but generally a very desiredproperty. Also the property that every message that is sent, will ultimately be received is a typical propertyof a system. There are three main reasons to formulate properties of systems: • Reactive systems are often so complex that its behaviour cannot be neatly characterised. Only certain properties can be characterised. For instance in a leader election protocol processes can negotiate to select one, and only one, leader. In the more advanced protocols it is very hard or even not a priori determined to predict which process will become the leader [14]. Hence, describing the behaviour of the protocol is hard. It is only possible to denote the property that exactly one leader will be chosen. • In the early design stages, it is unclear what the behaviour of a system will be. Hence, writing down basic properties can help to establish some of the essential aspects of the system behaviour before commencing a detailed behavioural design. In UML use cases are used for this purpose. They are examples of potential runs of the system. The property language described here allows use cases to be denoted, but also allows to denote properties which all runs must adhere to. • It is very common that behavioural descriptions contain mistakes. By checking that a behavioural specification satisfies desirable properties, an extra safeguard is built in to guarantee the correctness of the specification.It is not easy to characterise the properties of a system. It often happens that what initially appears to be aneat and global property, turns out to be a property that is only valid most of the time. There are exceptionsthat require the property to be relaxed. Verification of the property is generally the only way to bring suchexceptions to light. Techniques to do so are treated in chapter 14.6.1 Hennessy-Milner logicHennessy-Milner logic is the underlying modal logic for our property language [30]. Its syntax is given bythe following BNF grammar: φ ::= true | false | ¬φ | φ ∧ φ | φ ∨ φ | φ → φ | a φ | [a]φ.The modal formula true is true in each state of a process and false is never true. The connectives ∧ (and),∨ (or) and ¬ (not) have their usual meaning. E.g., the formula φ1 ∧ φ2 is valid wherever both φ1 and φ2hold. It is perfectly valid to use other connectives from propositional logic such as implication (→), asthese can straightforwardly be expressed using the connectives above. The diamond modality a φ is valid whenever an a-action can be performed such that φ is valid afterthis a has been done. So, the formula a b c true expresses that a process can do an a followed by bfollowed by c. 71
  • Using the connectives more complex properties can be formulated. Expressing that after doing an aaction both a b and c must be possible is done by the formula a ( b true ∧ c true). Expressing that afteran a action no b is possible can be done by a ¬ b true. The box modality [a]φ is more involved. It is valid when for every action a that can be done, φ holdsafter doing that a. So, the formula [a] b true says that whenever an a can be done, a b action is possibleafterwards. The formula [a]false says that whenever an a is done, a situation is reached where false isvalid. As this cannot be, the formula expresses that an action a is not possible. Likewise, [a][b]false holdswhen a trace a b does not exist. Clearly, [a]φ and a φ express different properties. A good way to understand the differences is bygiving two transition systems, one where a φ holds and [a]φ is invalid, and vice versa, one where [a]φ isvalid, and a φ is invalid. In the first transition system below a φ is valid and [a]φ is not. In the secondtransition system the situation is reversed, namely [a]φ is valid a φ is not. Both formulas are true in thethird transition system and both are invalid in the fourth. a a a a φ ¬φ φ ¬φIn the labelled transition system at the left, an a action is possible to a state where φ holds, and one to astate where φ does not hold. So, a φ is valid, and [a]φ is not. In the second labelled transition diagramthere is no a-transition at all, so certainly not one to a state where φ is valid. So, a φ is not valid. But alla-transitions (which are none) go to a state where φ is valid. So, [a]φ holds. In the third all a-transitionsgo to a state where φ is valid and in the fourth all a-transitions go to a state where φ does not hold. The following identities are valid for Hennessy-Milner formulas. These identities are not only useful tosimplify formulas, but can also help to reformulate a modal formula to check whether its meaning matchesintuition. For instance the formula ¬ a [b]false can be hard to comprehend. Yet the equivalent [a] b trueclearly says that whenever an action a can be done, an action b must be possible after that a. Note that theequations show that the box and diamond modalities are dual to each other, just like the ∧ and the ∨ areduals of each other. ¬ a φ = [a]¬φ ¬[a]φ = a ¬φ a false = false [a]true = true a (φ ∨ ψ) = a φ ∨ a ψ [a](φ ∧ ψ) = [a]φ ∧ [a]ψ a φ ∧ [a]ψ ⇒ a (φ ∧ ψ)Besides these identities, the ordinary identities of propositional logic are also valid.Exercise 6.1.1. 1. Give a modal formula that says that in the current state an a can be done, followed by a b. Moreover, after the a no c is allowed. 2. Give a modal formula that expresses that whenever an a action is possible in the current state, it cannot be followed by an action b or an action c. 3. Give a modal formula that expresses that whenever in the current state an a action can be done when a b is also possible, the a cannot be followed by a b. In other words the action a cancels a b.Exercise 6.1.2. Give an argument why the following two formulas are equivalent. a ( b true∨ c1 false), ¬[a] ([b]false∧[c2 ]true).Exercise 6.1.3. Consider formulas φ1 = a b true and φ2 = [a]( b true ∧ [c]false). Give processexpressions with transition systems where φ1 is valid and φ2 is invalid and vice versa. 72
  • 6.2 Regular formulasIt is often useful to allow more than just a single action in a modality. For instance to express that after twoarbitrary actions, a specific action must happen. Or to say that after observing one or more receive actions,a deliver must follow. A very convenient way to do this, as put forward by [38], is the use of regular formulas within modali-ties. Regular formulas are based on action formulas, which we define first: Action formulas have the following syntax: α ::= a1 | · · · |an | true | false | α | α ∩ α | α ∪ α.Action formulas define a set of actions. The formula a1 | · · · |an defines the set with only the multi-actiona1 | · · · |an in it. The formula true represents the set of all actions and the formula false represents theempty set. For example the modal formula true a true expresses that an arbitrary action followed by anaction a can be performed. The formula [true]false expresses that no action can be done. The connectives ∩, ∪ in action formulas denote intersection and union of sets of action. The notationα denotes the complement of the set of actions α with respect to the set of all actions. The formula a b ∪ c true says that an action other than an a can be done, followed by either a b or a c. The formula[a]false says that only an a action is allowed. The precise definitions of modalities with action formulas in them is the following. Let α be a set ofactions then: α φ= aφ [α]φ = [a]φ. a∈α a∈α Regular formulas extend the action formulas to allow the use of sequences of actions in modalities. Thesyntax of regular formulas, with α an action formula, is: R ::= ε | α | R·R| R+R | R⋆ | R+ .The formula ε represents the empty sequence of actions. So, [ε]φ = ε φ = φ. In other words, it is alwayspossible to perform no action and by doing so, one stays in the same state. The regular formula R1 ·R2 represents the concatenation of the sequences of actions in R1 and R2 .For instance, a·b·c true is the same as a b c true expressing that a sequence of actions a, b and ccan be performed. The regular formula R1 +R2 denotes the union of the sequences in R1 and R2 . So,[a·b + c·d]false expresses that neither the sequence a b nor the sequence c d is possible. The definitions of both operators is the following: R1 +R2 φ = R1 φ ∨ R2 φ [R1 +R2 ]φ = [R1 ]φ ∧ [R2 ]φ R1 ·R2 φ = R1 R2 φ [R1 ·R2 ]φ = [R1 ][R2 ]φ. All the modal formulas described up till now are rather elementary, and not of much use to formulaterequirements of real system behaviour. By allowing R⋆ and R+ this improves substantially, because theyallow iterative behaviour. The regular formula R⋆ denotes zero or more repetitions of the sequences in R. Similarly, the formulaR stands for one or more repetitions. So, a⋆ true expresses that any sequence of a actions is possible. +And [a+ ]φ expresses that the formula φ must hold in any state reachable by doing one or more actions a. Two formulas, the always and eventually modalities, are commonly used. The always modality is oftendenoted as ✷φ and expresses that φ holds in all reachable states. The eventually modality is written as✸φ and expresses that there is a sequence of actions that leads to a state in which φ holds. Using regularformulas these can be written as follows: ✷φ = [true ⋆ ]φ ✸φ = true ⋆ φ. The always modality is a typical instance of a so-called safety property. Such properties typically saythat something bad will never happen. A typical example is that two processes cannot be in a critical region 73
  • at the same time. Entering the critical region is modelled by the action enter and leaving the critical regionis modelled by an action leave. So, in a modal formula we want to say that it is impossible to do twoconsecutive enter s without a leave action in between: ⋆ [true ⋆ ·enter ·leave ·enter]false.Another typical safety property is that there is no deadlock in any reachable state: [true ⋆ ] true true. Liveness properties say that something good will eventually happen. For instance the following formulaexpresses that after sending a message, it can eventually be received: [send] true ⋆ ·receive true.Compare this to the following formula ⋆ [send·receive ] true ⋆ ·receive truewhich says that after a send a receive is possible as long as it has not happened.Exercise 6.2.1. Give modal formulas for the following properties: 1. As long as no error happens, a deadlock will not occur. 2. Whenever an a can happen in any reachable state, a b action can subsequently be done unless a c happens cancelling the need to do the b. 3. Whenever an a action happens, it must always be possible to do a b after that, although doing the b can infinitely be postponed.Exercise 6.2.2. Show that the identities [R1 ·(R2 + R3 )]φ = [R1 ·R2 +R1 ·R3 ]φ and R1 ·(R2 + R3 ) φ = R1 ·R2 +R1 ·R3 φ hold. This shows that regular formulas satisfy the left distribution of sequential compo-sition over choice, which justifies to say that regular formulas represent sequences.6.3 Fixed point modalitiesAlthough regular expressions are very expressive and suitable for stating most behavioural properties, theydo not suit every purpose. By adding explicit minimal and maximal fixed point operators to Hennessy-Milner logic, a much more expressive language is obtained. This language is called the modal µ-calculus.As a sign of its expressiveness, it is possible to translate regular formulas to the modal µ-calculus. However,the expressiveness comes at a price. Formulating properties using the modal µ-calculus is far from easy. The modal µ-calculus in its basic form is given by the following syntax. Note that Hennessy-Milnerlogic is included in this language. φ ::= true | false | ¬φ | φ ∧ φ | φ ∨ φ | φ → φ | a φ | [a]φ | µX.φ | νX.φ | X.The formula µX.φ is the minimal fixed point and νX.φ stands for the maximal fixed point. Typically, thevariable X is used in fixed points, but other capitals, such as Y , Z are used as well. A good way to understand fixed point modalities is by considering X as a set of states. The formulaµX.φ is valid for all those states in the smallest set X that satisfies the equation X = φ, where X generallyoccurs in φ. Here we abuse notation, by thinking of X as the set of states where φ is valid. Similarly, νX.φis valid for the states in the largest set X that satisfies X = φ. We can illustrate this by looking at two simple fixed point formulas, namely µX.X and νX.X. So, weare interested in respectively the smallest and largest set of states X that satisfies the equation X = X.Now, any set satisfies this equation. So, the smallest set to satisfy it, is the empty set. This means thatµX.X is not valid for any state. This is equivalent to saying that µX.X = false. The largest set to satisfythe equation X = X is the set of all states. So, νX.X is valid everywhere. In other words, νX.X = true. As another example consider the formulas µX. a X and νX. a X. One may wonder whether thesehold for state s in the following transition system: 74
  • a sThe only sets of states to be considered are the empty set X = ∅ and the set of all states X = {s}. Bothsatisfy the ‘equation’ X = a X. Namely, if X = ∅, then the equation reduces to false = a false, whichis valid. If X = {s} it is also clear that this equation holds. So, µX. a X is valid for all states in the empty set. Hence, this formula is not valid in s. However,νX. a X is valid for all states in the largest set, being {s} in this case. So, νX. a X is valid. An effective intuition to understand whether or not a fixed point formula holds is by thinking of it asa graph to be traversed, where the fixed point variables are states and the modalities a and [a] are seenas transitions. A formula is true when it can be made true by passing a finite number of times throughthe minimal fixed point variables, whereas it is allowed to traverse an infinite number of times throughthe maximal fixed point variables. In the example with a single a-loop above, the formulas µX. a Xand νX. a X can only be made true by passing an infinite number of times through X and/or s. So, theminimal fixed point formula does not hold, and the maximal one is valid. Safety properties express that some undesired behaviour will never happen. These properties are gener-ally formulated using the maximal fixed point operator. Dually, liveness properties say that something goodwill happen within a finite number of steps. These are formulated using the minimal fixed point operator. Consider the formulas µX.φ and νX.φ. As the maximal fixed point formula is valid for the largest setof states satisfying X = φ and the minimal fixed point only for the smallest set, νX.φ is valid wheneverµX.φ is. This can concisely be formulated as follows: µX.φ ⇒ νX.φ.We use here a double implication arrow to stress that we are dealing with two formulas. If the first is true,then the second is true. The minimal and maximal fixed point operators are each other’s duals. This boils down to the followingtwo equations: ¬νX.φ = µX.¬φ ¬µX.φ = νX.¬φ.Note that using these equations it is always possible to remove the negations from modal formulas, providedthey have solutions (i.e. variables occur in the scope of an even number of negations). In order to be sure that a fixed point µX.φ or νX.φ exists, X must occur positively in φ. This meansthat X in φ must be preceded by an even number of negations. For counting negations φ1 → φ2 ought tobe read as ¬φ1 ∨ φ2 . So, for instance µX.¬X and νX.¬([a]¬X ∨ X) are not allowed. In the first case,the variable X is preceded by one negation. But there is no set of states that is equal to its complement.Therefore there is no solution for the equation ‘X = ¬X’, and certainly no minimal solution. So, µX.¬Xis not properly defined. In the second formula the first occurrence of X is preceded by two negations, whichis ok, but the second is preceded by only one. In this case the formula has only a well defined meaning ontransition systems without states with outgoing a-transitions. The formula νX.¬([a]¬X ∨ ¬X) has a welldefined solution as both occurrences of the variable X are preceded by an even number of negations. Regular formulas containing a ⋆ or a + are straightforwardly translated to fixed point formulas. Thetranslation is: R⋆ φ = µX.( R X ∨ φ) [R⋆ ]φ = νX.([R]X ∧ φ) R+ φ = R R⋆ φ [R+ ]φ = [R][R⋆ ]φNote that with the rules given above every regular formula can be translated to a fixed point modal formula.From a strictly formal standpoint, regular formulas are unnecessary. However, they turn out to be a verypractical tool to formulate many commonly occurring requirements on practical systems. In the previous section we have seen that ✸φ means that φ can eventually become valid. More precisely,there is a run starting in the current state on which φ becomes valid. Very often a stronger property isrequired, namely that φ will eventually become valid along every path. The formula to express this is: µX.([true]X ∨ φ). 75
  • Strictly speaking, this formula will also become true for paths ending in a deadlock, because in such astate [true]X becomes valid. In order to avoid this anomaly, the absence of a deadlock must explicitly bementioned: µX.(([true]X ∧ true true) ∨ φ).A variation of this is that an a action must unavoidably be done, provided there is no deadlock before theaction a. µX.[a]X.In order to express that a must be done anyhow, the possibility for a deadlock before an action a mustexplicitly be excluded. This can be expressed by the following formula: µX.([a]X ∧ true true).The last two formulas are not valid for the following transition system. The reason is that the b can infinitelyoften be done, and hence, an a action can be avoided. b aThe formula µX.([a]X ∨ a true) is valid in the initial state of the previous transition system. So, thistransition system distinguishes between the last formula and the two before that. Until now, we have only addressed fixed point formulas where the fixed point operators are used in astraightforward way. However, by nesting fixed point operators, a whole new class of properties can bestated. These properties are often called fairness properties, because these can express that some actionmust happen, provided it is unboundedly often enabled, or because some other action happens only abounded number of times. Consider for instance the formula µX.νY.(( a true ∧ [b]X) ∨ (¬ a true ∧ [b]Y )). (6.1)It says that from the states on each infinite b-trail, there are only a finite number of states where a-transitionsare possible. This is caused by the minimal fixed point operator before the X. The X can only finitelyoften be ‘traversed’, and this exactly occurs in a state where an a action is possible. The variable Y , canbe traversed infinitely often, as it is preceded by a maximal fixed point, meaning that infinite b sequenceswhere no a is possible are allowed. By exchanging the minimal and maximal fixed point symbol, the meaning of the formula can becomequite different. The formula νX.µY.(( a true ∧ [b]X) ∨ (¬ a true ∧ [b]Y ))says that on each sequence states reachable via b actions, only finite substretches of states can not have anoutgoing a transition. So, typically, if a is enabled in every state, this formula holds. This shows that thisformula is not equal to (6.1), which in invalid if a is always enabled.Exercise 6.3.1. Consider the formulas φ1 = µX.[a]X and φ2 = νX.[a]X. If possible, give transitionsystems where φ1 is valid in the initial state and φ2 is not valid and vice versa.Exercise 6.3.2. Give a labelled transition system that distinguishes between the following formulas µX.([a]X ∨ true ⋆ ·a true) and µX.[a]X.Exercise 6.3.3. Are the following formulas equivalent, and if not, explain why: ⋆ [send·receive ] true ⋆ ·receive true and [send ]µX.([receive]X ∧ true true).Exercise 6.3.4. What do the following formulas express: µX.νY.([a]Y ∨ [b]X) and νX.µY.([a]Y ∨ [b]X).Is there a process that shows that these formulas are not equivalent? 76
  • 6.4 Modal formulas and labelled transition systemsHow can one establish whether a modal formula is valid in the initial state of a labelled transition system?In chapter 15 the semantics of a modal formula is given using which the validity can be calculated and inchapter 14 it is explained how one can calculate this using parameterised boolean equation systems. In thissection a more direct method is sketched to verify a modal formula on a transition system. For Hennessy-Milner formulas the procedure is straightforward. Consider a formula φ and a labelledtransition system. Label each node with all subformulas of φ that hold in that node. This is most conve-niently done by working from the smallest to the larger subformulas. So, as a first step each node is labelled with true, and none with false. Each node is labelled with aformula φ, iff it is not labelled with φ. A state is labelled with φψ if it is labelled with φ and ψ, andsimilarly for φ ∨ ψ. A state is labelled with a φ iff there is an outgoing a transition to a state where φholds. Similarly, a node is labelled with [a]φ iff all outgoing a transitions go to states where φ holds. Notethat as the number of subformulas is linear in the size of the formula the complexity of this procedure islinear in the product of the size of the transition system and that of the formula. true, a b true true a a a a true true, b true true true, b true b b true true Figure 6.1: Labelling an LTS with subformulas Consider the transition systems in figure 6.1. At the left the formula a b true is verified, and at theright the formula [a] b true. It is clear that in the initial states, the first formula holds, and the second doesnot In case there are fixed point symbols the procedure is slightly more complicated. We only sketch analgorithm for formulas with only one fixed point symbol. For a minimal fixed point µX.φ it is initiallyassumed that X does not hold, i.e., no state is initially labelled with X. Then states are labelled by validformulas, as is done above. When all states that are labelled with φ are also labelled with X, then thelabelling stops and all states are labelled with the formulas that hold in that state. Whenever a state islabelled with φ and not with X, it is also labelled with X and the state labelling is updated to reflect thischange. This procedure terminates, in the sense that at some point no new states occur which are labelledwith φ and not with X. true, X, a X, true true a X ∨ b true a a a true, X, a X, true true a X ∨ b true a a a true, a X, true, X, a X, true a X ∨ b true a X ∨ b true a a a true, b true, true, X, b true, true, X, b true, a X ∨ b true a X ∨ b true a X ∨ b true b b b true true true Figure 6.2: Labelling an LTS with subformulas As an example consider figure 6.2 and the formula µX.( a X ∨ b true), which expresses that thereis a finite sequence of a actions after which a b is possible. The first labelling is at the left, where no state 77
  • is labelled with X. Note that the one but last state gets the labelling a X ∨ b true, but this is because itis also labelled with b true. So, as the one but last state is labelled with a X ∨ b true, it will also belabelled with X. After a next round of updating the labels one obtains the diagram in the middle of figure6.2. Now, it can be observed that a X ∨ b true holds in the third state. So, it is labelled with X, too.After repeating this procedure two more steps, it can be seen in the diagram at the right that X holds in theinitial state. true, X, [a]X, true, a true a true, [a]X ∧ a true a a true, X, [a]X, true, a true a true, [a]X ∧ a true a a true, X, [a]X true, [a]X Figure 6.3: Labelling an LTS with subformulas For a maximal fixed point νX.φ it is assumed that X holds initially in all states. The same procedureas for the minimal fixed point is followed, except that X is removed from a state if φ does not hold in thatstate after a labelling step. The formula [a⋆ ] a true or equivalently νX.([a]X ∧ a true) says that therecan always one more a be done after an arbitrary a-sequence. It is checked in figure 6.3. Note that in thelast state [a]X ∧ a true is not true and therefore X must be removed. Subsequently, X must be removedfrom the second and finally also from the initial state. The final labelling is shown in the figure at the right.The formula does not hold. In all states in a labelled transition system that are strongly bisimilar the same modal formulas holdprovided that the transition system has a finite number of transitions leaving each state. Therefore, it can beuseful to first reduce a transition system modulo strong bisimulation, before calculating the modal formula. ¬ a ( b true∧ c true) a ( b true∧ c true) a a a b c b c Figure 6.4: Two non-bisimilar transition systems with distinguishing formulas The reverse also holds. When the initial states of two transition systems are not bisimilar, there is amodal formula that is valid in one state and invalid in the other. This formula is called the distinguishingformula. It can be helpful to understand why two transition systems are not bisimilar. For instance the wellknown non bisimilar transition systems in figure 6.4 have φ = a ( b true ∧ c true) as it distinguishingformula. The modal formula φ holds at the right, but not at the left.Exercise 6.4.1. Prove by applying the algorithm from this section that the formula a ( b true∧ c true)does not hold in the figure at the left of figure 6.4 and holds at the right.Exercise 6.4.2. Provide distinghuishing formulas for the three pairs of transition systems in exercise 2.3.6.6.5 Modal formulas with dataSimilar to the situation with processes, we also need data, and sometimes time in modal formulas to de-scribe real world phenomena. 78
  • Operator Rich Plain hidden action τ tau multi-action | _|_ set of all multi-actions, true true true empty set of multi-actions, false false false complement of set of multi-actions, negation of a modal formula !_ intersection of sets of multi-actions ∩ _&&_ union of sets of multi-actions ∩ _||_ universal quantification ∀d:D. forall d:D._ existential quantification ∃d:D. exists d:D._ concatenation of regular formulas · _._ choice between regular formulas + _+_ ⋆ reflexive and transitive closure _* + transitive closure _+ conjunction of modal formulas ∧ _&& _ disjunction of modal formulas ∨ _||_ implication between modal formulas → _=>_ diamond modality <_>_ box modality [] [_]_ minimal fixed point µX(d1 :D1 := t1 , . . . , dn :Dn := tn ). mu X(d1:D1=t1,...,dn:Dn=tn)._ maximal fixed point νX(d1 :D1 := t1 , . . . , dn :Dn := tn ). nu X(d1:D1=t1,...,dn:Dn=tn)._ Table 6.1: Translation of modal operators to plain text Modal formulas are extended with data in three ways, similar to processes. In the first place, modalvariables can have arguments. Secondly, actions can carry data arguments and time stamps. And finally,existential and universal quantification is possible. The extensions lead to the following extensions of thesyntax, where α stands for a multi-action, R represents a regular formula, φ stands for a modal formulaand af stands for an action formula. In table 6.1 the translation of the operators to plain ascii notation isprovided, which is used in the tools. α ::= τ | a(t1 , . . . , tn ) | α|α. af ::= t | true | false | α | af | af ∩ af | af ∪ af | ∀d:D.af | ∃d:D.af . R ::= ε | af | R·R | R+R | R⋆ | R+ . φ ::= true | false | t | ¬φ | φ ∧ φ | φ ∨ φ | φ → φ | ∀d:D.φ | ∃d:D.φ | R φ | [R]φ | µX(d1 :D1 :=t1 , . . . , dn :Dn :=tn ).φ | νX(d1 :D1 :=t1 , . . . , dn :Dn :=tn ).φ | X(t1 , . . . , tn ).So, any expression t of sort B is an action formula. If t is true, it represents the set of all actions and if false,it represents the empty set. The action formula ∃n:N.a(n) represents the set of actions {a(n) | n ∈ N}.More generally, the action formula ∃d:D.af represents d:D af . Dually, ∀d:D.af represents d:D af . These quantifications are useful to express properties involving certain subclasses of actions. E.g., theformula [true ⋆ ·∃n:N.error (n)]µX.([shutdown]X ∧ true true)says that whenever an error with some number n is observed, a shutdown is inevitable. There can be a side condition on the error, for instance using a predicate fatal . A shutdown should onlyoccur if the error is fatal: [true ⋆ ·∃n:N.(fatal (n) ∩ error (n))]µX.([shutdown ]X ∧ true true). 79
  • If fatal (n) holds, fatal (n) is true and represents the set of all actions. The expression fatal (n) ∩ error (n)represents the set with exactly the action error (n), provided fatal (n) is valid. So, ∃n:N.(fatal (n) ∩error (n)) is exactly the set of all those error (n) actions which are fatal. Note that fatal , being a predicate,and error , being an action, are very different objects. Reversely, one may be interested in saying that as long as no fatal error occurs, there will be no dead-lock. [(∀n:N.(fatal (n) ∩ error (n)))⋆ ] true true.In such cases the universal quantifier can be used in action formulas. In modal formulas it is also allowed to use universal and existential quantifications over data with thestandard meaning. So, ∀d:D.φ is true, if φ holds for all values from the domain D substituted for d in φ.For the existential quantifier, φ only needs to hold for some value in D substituted for d. Quantificationallows to use data that stretches throughout a formula. For instance saying that the same value is neverdelivered twice can be done as follows: ∀n:N.[true ⋆ ·deliver (n)·true ⋆ ·deliver (n)]false.Note that this is not possible using the quantifiers of action formulas, as their scope is only limited to asingle action formula. Using the existential quantifier we can express that some action takes place, about which we do nothave all information. For instance, after sending a message that message can eventually be delivered withsome error code n. The error code is irrelevant for this requirement, but as it is a parameter of the actiondeliver, it must be included in the formula. ∀m:Message.[true ⋆ ·send (m)] true ⋆ ·∃n:N.deliver (m, n) true. Note that it does not really matter where the quantifiers are put. I.e., ∀d:D.[true ⋆ ·a(d)]false = [true ⋆ ]∀d:D[a(d)]false A very powerful feature is the ability of putting data in fixed point variables as parameters. Usingthis it is possible to for instance count the number of events. Saying that a buffer may never deliver moremessages than it received can be done as follows: νX(n:N:=0).[deliver ∪ receive]X(n) ∧ [receive]X(n+1) ∧ [deliver ](n > 0 ∧ X(n−1)).Here n counts the number of received messages that have not been delivered. The notation n:N:=0 saysthat n is a natural number that is initially set to 0. The core of the formula is in its last conjunct, which isfalse if a deliver is possible while n≈0. This conjunct then becomes false, turning the whole modal formulainto false. Note that the fixed point variables that depend on data, are effectively variables ranging over functionsfrom data elements to sets of states. Although this turns modal formulas into rather advanced mathematicalobjects, the use of data in variables is generally quite intuitive and straightforward. Another interesting example consists of a merger process (see figure 6.5). It reads two streams ofnatural numbers, and delivers a merged stream. Reading goes via actions r1 and r2 and data is deliveredvia stream s. The property that the merger must satisfy is that as long as the input streams at r1 and r2 areascending, the output must be ascending too. This can be formulated as follows. The variables in1 , in2and out contain the last numbers read and delivered. νX(in 1 :N:=0, in 2 :N:=0, out:N:=0).∀l:N.([r1 (l)](l ≥ in 1 → X(l, in 2 , out ))∧ [r2 (l)](l ≥ in 2 → X(in 1 , l, out)) ∧ [s(l)](l ≥ out ∧ X(in 1 , in 2 , l))).It does not appear to be possible to phrase this property without using data in the fixed point variables.Exercise 6.5.1. Specify a unique number generator that works properly if it does not generate the samenumber twice. 80
  • r1 Merger s r2 Figure 6.5: A merger reading natural numbers at r1 and r2 and delivering at sExercise 6.5.2. Express the property that a sorting machine only delivers sorted arrays. Arrays are repre-sented by a function f : N → N.Exercise 6.5.3. Specify that a store with product types of sort Prod is guaranteed to refresh each product.For each product type there can be more instances in the store. The only way to see this, is that thedifference in the number of enter (p) and leave(p) is always guaranteed to become zero within a finitenumber of steps.6.6 EquationsIn tables 6.2 and 6.3 identities are enumerated that hold between modal formulas. Although most validequations are contained in the list it is not known whether it is complete in the sense that all valid identitiesbetween logic formulas can be derived by it. Note that the identity a ∩ b φ = a φ ∧ b φ is not valid. This is caused by the fact that a ∩ b is theempty action formula, i.e., false. The formula false φ equals false as it says that in the current state a stepcan be done, not carrying any action label. Such a step does not exist (τ is an action label). The formula a φ ∧ b φ is not equal to false. It for instance holds for the process a + b. A weaker version, in casu a ∩ b φ ⇒ a φ ∧ b φ is valid. Here we cannot use equality, but useimplication between formulas, denoted as ⇒, instead. Note that implication in formulas → and → relate inthe samen way as data equality ≈ and =. We als write φ ⇐ ψ between formulas, as a shorthand to expressthat ψ implies φ. In chapter 9 we will give a proof system that express that deals with a.o., ⇒. The following implications cannot be formulated as identities, too: [af 1 ∩ af 2 ]φ ⇐ [af 1 ]φ ∨ [af 2 ]φ, ∀d:D.AF (d) φ ⇒ ∀d:D. AF (d) φ, [∀d:D.AF (d)]φ ⇐ ∃d:D.[AF (d)]φ. 81
  • Proposition logicφ∧ψ =ψ∧φ φ∨ψ =ψ∨φ(φ ∧ ψ) ∧ χ = φ ∧ (ψ ∧ χ) (φ ∨ ψ) ∨ χ = φ ∨ (ψ ∨ χ)φ∧φ=φ φ∨φ=φ¬true = false ¬false = trueφ ∧ true = φ φ ∨ true = trueφ ∧ false = false φ ∨ true = φφ ∧ (ψ ∨ χ) = (φ ∧ ψ) ∨ (φ ∧ χ) φ ∨ (ψ ∧ χ) = (φ ∨ ψ) ∧ (φ ∨ χ)¬(φ ∧ ψ) = ¬φ ∨ ¬ψ ¬(φ ∨ ψ) = ¬φ ∧ ¬ψ¬¬φ = φ φ → ψ = ¬φ ∨ ψφ ⇒ ψ = ¬φ ∨ ψ φ⇔ψ =φ⇒ψ∧ψ ⇒φPredicate logic∀d:D.φ = φ ∃d:D.φ = φ¬∀d:D.Φ(d) = ∃d:D.¬Φ(d) ¬∃d:D.Φ(d) = ∀d:D.¬Φ(d)∀d:D.(Φ(d)∧Ψ(d)) = ∀d:D.Φ(d)∧∀d:D.Ψ(d) ∃d:D.(Φ(d)∨Ψ(d)) = ∃d:D.Φ(d)∨∃d:D.Ψ(d)∀d:D.(Φ(d)∨ψ) = ∀d:D.Φ(d) ∨ ψ ∃d:D.(Φ(d)∧ψ) = ∃d:D.Φ(d) ∧ ψ∀d:D.Φ(d) ⇒ Φ(e) Φ(e) ⇒ ∃d:D.Φ(d)Action formulastrue = false false = trueα1 ∪ α2 = α1 ∩ α2 α1 ∩ α2 = α1 ∪ α2∃d:D.A(d) = ∀d:D.A(d) ∀d:D.A(d) = ∃d:D.A(d)Hennessy-Milner logic¬ a φ = [a]¬φ ¬[a]φ = a ¬φ a false = false [a]true = true a (φ ∨ ψ) = a φ ∨ a ψ [a](φ ∧ ψ) = [a]φ ∧ [a]ψ a φ ∧ [a]ψ ⇒ a (φ ∧ ψ) [a](φ ∨ ψ) ⇒ a φ ∨ [a]ψ Table 6.2: Equivalences between modal formulas (part I) 82
  • Fixed point equationsµX.φ ⇒ νX.φ¬µX.φ = νX.¬φ ¬νX.φ = µX.¬φµX.φ(X) = φ(µX.φ(X)) νX.φ(X) = φ(νX.φ(X))if φ(ψ) ⇒ ψ then µX.φ(X) ⇒ ψ if ψ ⇒ φ(ψ) then ψ ⇒ νX.φ(X)Regular formulas ε φ=φ [ε]φ = φ false φ = false [false]φ = true af 1 ∪ af 2 φ = af 1 φ ∨ af 2 φ [af 1 ∪ af 2 ]φ = [af 1 ]φ ∧ [af 2 ]φ af 1 ∩ af 2 φ ⇒ af 1 φ ∧ af 2 φ [af 1 ∩ af 2 ]φ ⇐ [af 1 ]φ ∨ [af 2 ]φ ∃d:D.AF (d) φ = ∃d:D. AF (d) φ [∃d:D.AF (d)]φ = ∀d:D.[AF (d)]φ ∀d:D.AF (d) φ ⇒ ∀d:D. AF (d) φ [∀d:D.AF (d)]φ ⇐ ∃d:D.[AF (d)]φ R1 + R2 φ = R1 φ ∨ R2 φ [R1 + R2 ]φ = [R1 ]φ ∧ [R2 ]φ R1 ·R2 φ = R1 R2 φ [R1 ·R2 ]φ = [R1 ][R2 ]φ R⋆ φ = µX.( R X ∨ φ) [R⋆ ]φ = νX.([R]X ∧ φ) R+ φ = R R⋆ φ [R+ ]φ = [R][R⋆ ]φ¬ R φ = [R]¬φ ¬[R]φ = R ¬φ[R]true = true R false = false R (φ ∨ ψ) = R φ ∨ R ψ [R](φ ∧ ψ) = [R]φ ∧ [R]ψ R φ ∧ [R]ψ ⇒ R (φ ∧ ψ) [R](φ ∨ ψ) ⇒ R φ ∨ [R]ψ Table 6.3: Equivalences between modal formulas (part II) 83
  • 84