Probabilistic CTL

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Probabilistic Computational Tree Logic is an extension of CTL which allows for probabilistic quantification of described properties. It has been defined in the paper by Hansson and Jonsson. PCTL is a useful logic for stating soft deadline properties, e.g. "after a request for a service, there is at least a 98% probability that the service will be carried out within 2 seconds". Akin CTL suitability for model-checking PCTL extension is widely used as a property specification language for probabilistic model checkers.

[edit] PCTL Syntax

One of the possible syntax of PCTL is defined as follows:

$\phi ::= p | \neg p | \phi \lor \phi | \phi \land \phi | \mathcal{P}_{\sim\lambda}(\phi \mathcal{U} \phi) | \mathcal{P}_{\sim\lambda}(\square\phi)$

Therein, $\sim \in \{ <, \leq, =, \geq, > \}$ is comparison operator and $λ$ is a probability threshold.
Formulas of PCTL are interpreted over discrete Markov chains. An interpretation structure is a quadruple $K = \langle S, s^i, \mathcal{T}, L \rangle$ , where

$S$ is a finite set of states,
$s^i \in S$ is an initial state,
$\mathcal{T}$ is a transition probability function, $\mathcal{T} : S \times S \to [0,1]$ , such that for all $s \in S$ we have $\sum_{s'\in S} \mathcal{T}(s,s')=1$ , and
$L$ is a labeling function, $L:S\to2^A$ , assigning atomic propositions to states.

A path $σ$ from a state $s 0$ is an inifite sequence of states $s_0 \to s_1 \to \dots \to s_n \to \dots$ . The n-th state of the path is denoted as $σ[n]$ and the prefix of $σ$ of length $n$ is denoted as $\sigma\uparrow n$ .

[edit] Probability Measure

A probability measure $μ m$ of the set of path with the common prefix of length $n$ is equal to the product of transitions probabilitites along the prefix of the path:

$\mu_m(\{\sigma \in X : \sigma\uparrow n = s_0 \to \dots \to s_n \}) = \mathcal{T}(s_0,s_1) \times\dots\times\mathcal{T}(s_{n-1},s_n)$

For $n = 0$ the probability measure is equal to $\mu_m(\{\sigma \in X : \sigma\uparrow 0 = s_0 \}) = 1$ .

[edit] Satisfaction relations

Satisfaction relations $s \models_K f$ , $\sigma \models_K f$ are inductively defined as follows:

$s \models_K a$ if and only if $a \in L(s)$ ,
$s \models_K \neg f$ if and only if not $s \models_K f$ ,
$s \models_K f_1 \lor f_2$ if and only if $s \models_K f_1$ or $s \models_K f_2$ ,
$s \models_K f_1 \land f_2$ if and only if $s \models_K f_1$ and $s \models_K f_2$ ,
$s \models_K \mathcal{P}_{\sim\lambda}(f_1 \mathcal{U} f_2)$ if and only if $\mu_m(\{\sigma : \sigma[0] = s \land (\exists i)\sigma[i] \models_K f_2 \land (\forall 0 \leq j < i) \sigma[j] \models_K f_1\}) \sim \lambda$ , and
$s \models_K \mathcal{P}_{\sim\lambda}(\square f)$ if and only if $\mu_m(\{\sigma : \sigma[0] = s \land (\forall i \geq 0)\sigma[i] \models_K f\}) \sim \lambda$ .