From d3457f19abcda5ca030d17ebc4589b80e0c1f5a1 Mon Sep 17 00:00:00 2001 From: Matthias Volk Date: Wed, 13 Dec 2023 16:25:57 +0100 Subject: [PATCH] Removed documentation on pGCL as support was removed --- _layouts/home.html | 1 - documentation/background/languages.md | 4 ---- documentation/usage/running-storm.md | 16 ++-------------- 3 files changed, 2 insertions(+), 19 deletions(-) diff --git a/_layouts/home.html b/_layouts/home.html index 5d35278eca..eb43c628b6 100644 --- a/_layouts/home.html +++ b/_layouts/home.html @@ -127,7 +127,6 @@

Input languages

  • JANI
  • GSPNs
  • DFTs
    • -
  • cpGCL
  • explicit
    • diff --git a/documentation/background/languages.md b/documentation/background/languages.md index d30bd7e557..bad73672a6 100644 --- a/documentation/background/languages.md +++ b/documentation/background/languages.md @@ -39,10 +39,6 @@ The [GreatSPN editor](http://www.di.unito.it/~amparore/mc4cslta/editor.html){:ta Dynamic Fault Trees are given in the so-called [Galileo Format](https://www.cse.msu.edu/~cse870/Materials/FaultTolerant/manual-galileo.htm#Editing%20in%20the%20Textual%20View){:target="_blank"}. The format is a simple textual format naming the root of the tree and then lists all nodes of the tree, together with the children of each node. A [rich collection](https://github.com/moves-rwth/dft-examples){:target="_blank"} of examples is available. -## cpGCL - -The conditional probabilistic guarded command language, cpGCL for short, is an extension of Dijkstra's guarded command language and pGCL {% cite HSM97 %} to also accommodate probabilistic choice and conditional observations. Examples can be found in some of the folders in the [JANI repository](#jani). For example, the famous coupon collector example encoded as a cpGCL program can be found [here](https://github.com/ahartmanns/jani-models/blob/master/CouponCollector/MultiAllowed/coupon_m_03_02.pgcl){:target="_blank"}. - ## Explicit In the spirit of [MRMC](http://www.mrmc-tool.org/){:target="_blank"}, Storm also supports input models specified in an explicit format. While the format closely resembles that of MRMC, it does not match exactly. Likewise, at the moment the model export of PRISM generates files that can be easily modified to be handled by Storm, but they still need to be adapted by hand. diff --git a/documentation/usage/running-storm.md b/documentation/usage/running-storm.md index 8234bc7b63..15febc3ee7 100644 --- a/documentation/usage/running-storm.md +++ b/documentation/usage/running-storm.md @@ -43,12 +43,6 @@ Storm takes [many languages]({{ '/documentation/background/languages.html' | rel storm-gspn-cli Running Storm on GSPNs - - pGCL - storm-pgcl - storm-pgcl-cli - Running Storm on pGCL - @@ -215,7 +209,7 @@ In case you also want to read the properties from the jani file, the option `--j #### Example 4 (Analysis of a rejection-sampling algorithm to approximate $$\pi$$) -Here, we are going to analyze a model of an algorithm that approximates $$\pi$$. It does so by repeated sampling according to a uniform distribution. While this model is a JANI model, the original model was written in [pGCL]({{ '/documentation/background/languages.html#cpgcl' | relative_url }}) and has been translated to JANI by Storm's `storm-pgcl` binary. The JANI model and the original pGCL code is available from the [JANI models repository](https://github.com/ahartmanns/jani-models){:target="_blank"}. +Here, we are going to analyze a model of an algorithm that approximates $$\pi$$. It does so by repeated sampling according to a uniform distribution. While this model is a JANI model, the original model was written in the probabilistic guarded command language (pGCL) and has been translated to JANI. The JANI model and the original pGCL code is available from the [JANI models repository](https://github.com/ahartmanns/jani-models){:target="_blank"}. {% include includes/show_model.html name="original pGCL program" class="jani_approxpi_pgcl" path="jani/approx_pi_00100_010_full.pgcl" %} @@ -231,7 +225,7 @@ $ storm --jani approx_pi_00100_010_full.jani --engine hybrid As we selected the *hybrid* engine, Storm builds the MDP in terms of a symbolic data structure ((MT)BDDs), hence the `(symbolic)` marker. For this representation, Storm also reports the sizes of the state and transition DDs in terms of the number of nodes. -The algorithm uses a sampling-based technique to approximate $$\pi$$. More specifically, it repeatedly (100 times in this particular instance) samples points in a square and checks whether they are in a circle whose diameter is the edge length of the square (which is called a hit). From this, we can derive $$\pi \approx 4 \frac{hits}{100}$$ (for more details, we refer to [this explanation](https://theclevermachine.wordpress.com/tag/rejection-sampling/){:target="_blank"}). We are therefore interested in the expected number of hits until termination of the algorithm (as all JANI models obtained from `storm-pgcl`, it has a transient Boolean variable `_ret0_` that marks termination of the pGCL program; this transient variable can be used as a label in properties): +The algorithm uses a sampling-based technique to approximate $$\pi$$. More specifically, it repeatedly (100 times in this particular instance) samples points in a square and checks whether they are in a circle whose diameter is the edge length of the square (which is called a hit). From this, we can derive $$\pi \approx 4 \frac{hits}{100}$$ (for more details, we refer to [this explanation](https://theclevermachine.wordpress.com/tag/rejection-sampling/){:target="_blank"}). We are therefore interested in the expected number of hits until termination of the algorithm. The program has a transient Boolean variable `_ret0_` that marks termination of the pGCL program; this transient variable can be used as a label in properties: ```console $ storm --jani approx_pi_00100_010_full.jani --engine hybrid --prop "Rmax=? [F \"_ret0_\"]" @@ -338,9 +332,3 @@ For additional command-line options see the help for GSPNs with: ```console $ storm-gspn --help gspn ``` - - -## Running Storm on pGCL - -{:.alert .alert-info} -Coming soon.