nergmada · nergmada · Jan 24, 2020 · Dec 23, 2019 · Dec 23, 2019 · Dec 23, 2019
diff --git a/docs/reference/planners/ACOPlan/main.md b/docs/reference/planners/ACOPlan/main.md
@@ -0,0 +1,31 @@
+---
+layout: default
+title: ACOPlan
+parent: Planners
+permalink: /ref/planners/acoplan
+nav_order: 5
+---
+# Ant Colony Optimisation (ACO) Plan
+
+Page Contributors: {% git_author %}
+
+tags: [IPC2011](/ref/planners/tags/ipc2011), [Satisfycing](/ref/planners/tags/satisfycing)
+
+Planner Quality: [14.9 out of 100](/ref/planners/rating)
+
+Year Published: 2009
+
+Paper: [ACOPlan: Planning with Ants](https://www.aaai.org/ocs/index.php/FLAIRS/2009/paper/download/116/276) [ Baioletti, M.  Milani, A. Poggioni, V. Rossi, F ]
+
+Preceded By: -
+
+ACOPlan is a planner based on the ant colony optimization framework, in which a colony of planning ants searches for near optimal solution plans with respect to an overall plan cost metric. This approach is motivated by the strong similarity between the process used by artificial ants to build solutions and the methods used by state–based planners to search solution plans. Planning ants perform a stochastic and heuristic based search by interacting through a pheromone model.
+
+## Support
+
+ACOPlan has not been tested with eviscerator as we could not find source code for the planner, or we couldn't get the source code to compile. ACOPlan appears to be targeted to solving classical planning problems, so likely does not support any temporal or numeric features in PDDL.
+
+
+## Downloading and Compiling ACOPlan
+
+No Source code could be located for ACOPlan
diff --git a/docs/reference/planners/ACOPlan/rating_justification.md b/docs/reference/planners/ACOPlan/rating_justification.md
@@ -0,0 +1,24 @@
+- **5 points** for classical coverage
+Awarded 5
+- **5 points** for classical optimal coverage
+Awarded 4 (near optimal planner)
+- **10 points** for temporal-numeric coverage
+Awarded 0
+- **5 points** for expressive features coverage (TILs, Action Costs, Processes, Events)
+Awarded 5 (supports Action costs)
+- **10 points** for a code publication on a popular public source control system (GitHub, BitBucket, Mercurial etc.)
+Awarded 0
+- **5 points** for including a readme in text/markdown format on how to compile the code for a defined system (e.g. here's how to compile in on Ubuntu/Fedora/MacOS/Windows etc)
+Awarded 0
+- **5 points** for including an automated script that is guaranteed to work on a developer defined base system with no pre-requisites installed
+Awarded 0
+- **5 points** for clear updates and bug reporting and patching processes 
+Awarded 0
+- **5 points** for a readme detailing the major contribution of the planner, and how to use it (e.g. command line/UI instructions)
+Awarded 0
+- **10 points** for an extended readme detailing supported PDDL features, as defined in this wiki or on the eviscerator testing tool - or equivalent table of support attached clearly to the planner or the software repo
+Awarded 0
+- **10 points** for code documentation that outlines structure and implementation of the code
+Awarded 0
+- **25 points** for IPC competitve ranking
+Award 0.9 (ranked 26th and 27th out of 27, ergo (27 - 26) / 27 * 25 = 0.9)
diff --git a/docs/reference/planners/Alien/main.md b/docs/reference/planners/Alien/main.md
@@ -0,0 +1,31 @@
+---
+layout: default
+title: Alien
+parent: Planners
+permalink: /ref/planners/alien
+nav_order: 5
+---
+# Alien Planner
+
+Page Contributors: {% git_author %}
+
+tags: [IPC2018](/ref/planners/tags/ipc2018), [Satisfycing](/ref/planners/tags/satisfycing)
+
+Planner Quality: [38.1 out of 100](/ref/planners/rating)
+
+Year Published: 2018
+
+Paper: [Alien: Return of Alien Technology to Classical Planning](https://ipc2018-classical.bitbucket.io/planner-abstracts/team33.pdf) [ Asai, M. ]
+
+Preceded By: -
+
+Alien puts an emphasis on preprocessing and low-level performance. This planner is intended to an alternative framework to The Fast Downward Planning [Link Needed], and gives an emphasis to improved preprocessing stages, code quality, and low-level performance. 
+
+## Support
+
+Alien planner has yet to be successfully tested with Eviscerator, although we believe it is possible and will attempt to conduct a test soon [test needed]. Alien states it is a classical planner in its IPC planner submission so most likely does not support temporal numerics
+
+
+## Downloading and Compiling Alien Planner
+
+Alien planner source code with a singularity container support can be found as part of a [BitBucket submission](https://bitbucket.org/ipc2018-classical/team33/src/ipc-2018-seq-sat/) to IPC2018
diff --git a/docs/reference/planners/Alien/rating_justification.md b/docs/reference/planners/Alien/rating_justification.md
@@ -0,0 +1,24 @@
+- **5 points** for classical coverage
+Awarded 5
+- **5 points** for classical optimal coverage
+Awarded 4 (near optimal planner)
+- **10 points** for temporal-numeric coverage
+Awarded 0
+- **5 points** for expressive features coverage (TILs, Action Costs, Processes, Events)
+Awarded 5 (supports Action costs)
+- **10 points** for a code publication on a popular public source control system (GitHub, BitBucket, Mercurial etc.)
+Awarded 10
+- **5 points** for including a readme in text/markdown format on how to compile the code for a defined system (e.g. here's how to compile in on Ubuntu/Fedora/MacOS/Windows etc)
+Awarded 2 (readme is poor but present)
+- **5 points** for including an automated script that is guaranteed to work on a developer defined base system with no pre-requisites installed
+Awarded 1 (not guaranteed to work but does include a makefile and singularity option)
+- **5 points** for clear updates and bug reporting and patching processes 
+Awarded 0
+- **5 points** for a readme detailing the major contribution of the planner, and how to use it (e.g. command line/UI instructions)
+Awarded 0
+- **10 points** for an extended readme detailing supported PDDL features, as defined in this wiki or on the eviscerator testing tool - or equivalent table of support attached clearly to the planner or the software repo
+Awarded 0
+- **10 points** for code documentation that outlines structure and implementation of the code
+Awarded 10
+- **25 points** for IPC competitve ranking
+Award 1.1 (ranked 23rd out of 23, ergo (23 - 22) / 23 * 25 = 1.1)
diff --git a/docs/reference/planners/AllPACA/main.md b/docs/reference/planners/AllPACA/main.md
@@ -0,0 +1,33 @@
+---
+layout: default
+title: AllPACA
+parent: Planners
+permalink: /ref/planners/allpaca
+nav_order: 5
+---
+# The AllPACA Planner: All Planners Automatic Choice Algorithm
+
+Page Contributors: {% git_author %}
+
+tags: [IPC2014](/ref/planners/tags/ipc2014), Optimal
+
+Planner Quality: [31.2 out of 100](/ref/planners/rating)
+
+Year Published: 2014
+
+Paper: Not found, please see the [IPC 2014 booklet](https://helios.hud.ac.uk/scommv/IPC-14/repository/booklet2014.pdf) for details on AllPACA
+
+Preceded By: Fast Downward
+
+The AllPACA planner is a portfolio planner, which automatically chooses which of several planners to run for the planning task that it is given. AllPACA is based on machine learning techniques, which attempt to choose the planner that will result in the fastest solution time, based on the features of the planning task. In the sequential optimal track, AllPACA was pre-trained on all planning tasks the sequential optimal track
+in all previous editions of the International Planning Competition. 
+
+For the learning track, AllPACA can also learn to predict planner performance on tasks from each domain during the training phase, and can additionally exploit domainspecific features.
+
+## Support
+
+AllPACA has not been tested with eviscerator as we could not find source code for the planner, or we couldn't get the source code to compile. Fast Downward forms the base planner of AllPACA, please refer to the Fast Downward [Link Needed] page for an idea of the supported features in this planner
+
+## Downloading and Compiling AllPACA
+
+No Source code could be located for AllPACA
diff --git a/docs/reference/planners/AllPACA/rating_justification.md b/docs/reference/planners/AllPACA/rating_justification.md
@@ -0,0 +1,24 @@
+- **5 points** for classical coverage
+Awarded 5
+- **5 points** for classical optimal coverage
+Awarded 5
+- **10 points** for temporal-numeric coverage
+Awarded 0
+- **5 points** for expressive features coverage (TILs, Action Costs, Processes, Events)
+Awarded 5 (supports Action costs)
+- **10 points** for a code publication on a popular public source control system (GitHub, BitBucket, Mercurial etc.)
+Awarded 0
+- **5 points** for including a readme in text/markdown format on how to compile the code for a defined system (e.g. here's how to compile in on Ubuntu/Fedora/MacOS/Windows etc)
+Awarded 0
+- **5 points** for including an automated script that is guaranteed to work on a developer defined base system with no pre-requisites installed
+Awarded 0
+- **5 points** for clear updates and bug reporting and patching processes 
+Awarded 0
+- **5 points** for a readme detailing the major contribution of the planner, and how to use it (e.g. command line/UI instructions)
+Awarded 0
+- **10 points** for an extended readme detailing supported PDDL features, as defined in this wiki or on the eviscerator testing tool - or equivalent table of support attached clearly to the planner or the software repo
+Awarded 0
+- **10 points** for code documentation that outlines structure and implementation of the code
+Awarded 0
+- **25 points** for IPC competitve ranking
+Award 16.2 (ranked 7th out of 17, ergo (17 - 6) / 17 * 25 = 16.2)
diff --git a/docs/reference/planners/Arvand/main.md b/docs/reference/planners/Arvand/main.md
@@ -0,0 +1,30 @@
+---
+layout: default
+title: Arvand
+parent: Planners
+permalink: /ref/planners/arvand
+nav_order: 5
+---
+# Arvand Planner
+
+Page Contributors: {% git_author %}
+
+tags: [IPC2011](/ref/planners/tags/ipc2011), [Satisfycing](/ref/planners/tags/satisfycing)
+
+Planner Quality: [22.6 out of 100](/ref/planners/rating)
+
+Year Published: 2011
+
+Paper: [Arvand: The Art of Random Walks](https://webdocs.cs.ualberta.ca/~mmueller/ps/arvand-art-of-random-walks.pdf) [ Nakhost, H. Muller, M. Valenzano, R. Xie, F. ]
+
+Preceded By: Fast Downward
+
+Arvand is a stochastic planner that uses Monte Carlo random walks (MRW) planning to balance exploration and exploitation in heuristic search. Arvand is built on top of the Fast Downward Planning System [link needed]
+
+## Support
+
+Arvand has not been tested with eviscerator as we could not find source code for the planner, or we couldn't get the source code to compile. Arvand appears to be targeted to solving classical planning problems, so likely does not support any temporal or numeric features in PDDL.
+
+## Downloading and Compiling Arvand
+
+No Source code could be located for Arvand
diff --git a/docs/reference/planners/Arvand/rating_justification.md b/docs/reference/planners/Arvand/rating_justification.md
@@ -0,0 +1,24 @@
+- **5 points** for classical coverage
+Awarded 5
+- **5 points** for classical optimal coverage
+Awarded 0
+- **10 points** for temporal-numeric coverage
+Awarded 0
+- **5 points** for expressive features coverage (TILs, Action Costs, Processes, Events)
+Awarded 5 (supports Action costs)
+- **10 points** for a code publication on a popular public source control system (GitHub, BitBucket, Mercurial etc.)
+Awarded 0
+- **5 points** for including a readme in text/markdown format on how to compile the code for a defined system (e.g. here's how to compile in on Ubuntu/Fedora/MacOS/Windows etc)
+Awarded 0
+- **5 points** for including an automated script that is guaranteed to work on a developer defined base system with no pre-requisites installed
+Awarded 0
+- **5 points** for clear updates and bug reporting and patching processes 
+Awarded 0
+- **5 points** for a readme detailing the major contribution of the planner, and how to use it (e.g. command line/UI instructions)
+Awarded 0
+- **10 points** for an extended readme detailing supported PDDL features, as defined in this wiki or on the eviscerator testing tool - or equivalent table of support attached clearly to the planner or the software repo
+Awarded 0
+- **10 points** for code documentation that outlines structure and implementation of the code
+Awarded 0
+- **25 points** for IPC competitve ranking
+Award 17.6 (ranked 9th out of 27, ergo (27 - 8) / 27 * 25 = 17.6)
diff --git a/docs/reference/planners/Arvandherd/main.md b/docs/reference/planners/Arvandherd/main.md
@@ -0,0 +1,30 @@
+---
+layout: default
+title: Arvandherd
+parent: Planners
+permalink: /ref/planners/arvandherd
+nav_order: 5
+---
+# Arvandherd Planner
+
+Page Contributors: {% git_author %}
+
+tags: [IPC2011](/ref/planners/tags/ipc2011), [Satisfycing](/ref/planners/tags/satisfycing)
+
+Planner Quality: [30 out of 100](/ref/planners/rating)
+
+Year Published: 2011
+
+Paper: [ArvandHerd: Parallel Planning with Portfolios](https://webdocs.cs.ualberta.ca/~mmueller/ps/2011/2011-arvandherd-IPC-booklet.pdf) [ Nakhost, H. Muller, M. Valenzano, R. Schaeffer, J. Sturtevant, N. ]
+
+Preceded By: Fast Downward
+
+ArvandHerd is a satisficing parallel planner that has been entered in the 2011 International Planning Competition (IPC 2011). It uses a portfolio-based approach where the portfolio contains four configurations of the Arvand planner and one configuration of the LAMA planner. Each processor runs a single planner, and the execution is mostly independent from the other processors so as to minimize overhead due to communication. ArvandHerd also uses the Aras plan-improvement system to improve plan quality
+
+## Support
+
+Arvandherd has not been tested with eviscerator as we could not find source code for the planner, or we couldn't get the source code to compile. Arvandherd appears to be targeted to solving classical planning problems, so likely does not support any temporal or numeric features in PDDL.
+
+## Downloading and Compiling Arvandherd
+
+No Source code could be located for Arvandherd
diff --git a/docs/reference/planners/Arvandherd/rating_justification.md b/docs/reference/planners/Arvandherd/rating_justification.md
@@ -0,0 +1,24 @@
+- **5 points** for classical coverage
+Awarded 5
+- **5 points** for classical optimal coverage
+Awarded 0
+- **10 points** for temporal-numeric coverage
+Awarded 0
+- **5 points** for expressive features coverage (TILs, Action Costs, Processes, Events)
+Awarded 5 (supports Action costs)
+- **10 points** for a code publication on a popular public source control system (GitHub, BitBucket, Mercurial etc.)
+Awarded 0
+- **5 points** for including a readme in text/markdown format on how to compile the code for a defined system (e.g. here's how to compile in on Ubuntu/Fedora/MacOS/Windows etc)
+Awarded 0
+- **5 points** for including an automated script that is guaranteed to work on a developer defined base system with no pre-requisites installed
+Awarded 0
+- **5 points** for clear updates and bug reporting and patching processes 
+Awarded 0
+- **5 points** for a readme detailing the major contribution of the planner, and how to use it (e.g. command line/UI instructions)
+Awarded 0
+- **10 points** for an extended readme detailing supported PDDL features, as defined in this wiki or on the eviscerator testing tool - or equivalent table of support attached clearly to the planner or the software repo
+Awarded 0
+- **10 points** for code documentation that outlines structure and implementation of the code
+Awarded 0
+- **25 points** for IPC competitve ranking
+Award 25 (ranked 1st out of 8, ergo (8 - 0) / 8 * 25 = 25)
diff --git a/docs/reference/planners/alltags.md b/docs/reference/planners/alltags.md
@@ -3,14 +3,14 @@ layout: default
 title: Planners by tag
 has_children: true
 parent: Planners
-permalink: /ref/planners/bytag
-nav_order: 0
+permalink: /ref/planners/tags
+nav_order: 1
 ---
 # List of all Planner tags
 
 Contributors: {% git_author %}
 
-Planners work in many different ways solving a variety of planning problems. The way the work under-the-hood dictates the types of problem they're capable of handling. In some cases a planner capable of handing classical planning problems has no ability to reason with temporal problems. To help users of this guide find planners that meet their needs, we tag planners with tags related to their abilities and various other notable characteristics. 
+Planners work in many different ways solving a variety of planning problems. The way they work under-the-hood dictates the types of problem they're capable of handling. In some cases a planner capable of handing classical planning problems has no ability to reason with temporal problems. To help users of this guide find planners that meet their needs, we tag planners with tags related to their abilities and various other notable characteristics. 
 
 The following is a list of tags
 
@@ -19,15 +19,14 @@ tag | Description
 Classical | Planners which perform classical predicate based planning
 Optimal | Planners which perform optimal planning
 Bounded Cost | Planners which find search to a bounded cost
-Satisficing | Planners which find solutions which satisfy the domain (non-optimal)
-Agile | 
+[Satisfycing](/ref/planners/tags/satisfycing) | Planners which find solutions which satisfy the domain (non-optimal)
 Temporal | Planners which plan temporal domains
 Numeric | Planners which plan numeric domains
 On Board | Planners designed to work on-board low power systems (integrated systems and robotics)
 Probabilistic | Planners which perform planning on probabilistic domains
 IPC2018 | Planners which took part in IPC 2018
 IPC2014 | Planners which took part in IPC 2014
-IPC2011 | Planners which took part in IPC 2011
+[IPC2011](/ref/planners/tags/ipc2011) | Planners which took part in IPC 2011
 IPC2008 | Planners which took part in IPC 2008
 IPC2006 | Planners which took part in IPC 2006
 IPC2004 | Planners which took part in IPC 2004

diff --git a/docs/reference/planners/atoz.csv b/docs/reference/planners/atoz.csv
@@ -1,7 +1,6 @@
 Planner,Description,Links,tags
 **A**,,,
-ACOPlan,,Guide Page \| Home Page \| Paper,
-ACOPlan2,,Guide Page \| Home Page \| Paper,
+ACOPlan/ACOPlan2,,Guide Page \| Home Page \| Paper,
 AIIPACA,,Guide Page \| Home Page \| Paper,
 alien,,Guide Page \| Home Page \| Paper,
 Arvand,,Guide Page \| Home Page \| Paper,