This is a Star Craft: Brood War bot for CSE 40971. This bot is a copy of the Marine Hell bot from the course documnetation that we are building on.

About Our Bot

Our bot makes use of behavior trees (one for strategy, one for economy, and one for research(coming soon)). The trees will be continuously executed throughout the game in a order that varies throughout gameplay depending on the situation. Our trees make use of a blackboard that keeps track of important up to date game data that determine what decisions will be made. Our trees will behave differently based on the race of our opponent, since good strategies drastically vary depending on the type of opponent. The trees consist of node objects that keep going down until an action(s) is executed. Once the action(s) is executed, the blackboard is informed that the tree has been ran and the tree will be ready to run again from the root. As we continue to grow our trees in detail and complexity we hope to have a bot that will make "BotBoi3000" a household name in the StarCraft universe.

If you would like to run this bot, we highly recommend simply downloading the BotBoi3000.jar file from the "executable jar file" folder, which will periodically be updated to our latest compiled build.

If, however, you are inclined to compile the .jar on your own, then please proceed with the following steps:

Compiling the Bot

1. If you have a Mac, please get a Windows (Pro version or above pls)

2. Now that you have Windows, download 32-bit JDK (choose the Windows x86 version). Make sure the install location is listed as the default "Program Files (x86)/Java/name_of_JDK_version" because we don't want to add support for changing from the default install location it's super vitally important for technical reasons you wouldn't understand.

3. Now that you have successfully installed a 32-bit JDK, please clone our repository to some location on your computer.

4. Now you need to download Chocolatey (if you don't already have it). Open Command Prompt Powershell as administrator and type in Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) (What's that you say? you don't trust me enough to run any command I tell you with administrator privledges on your machine? Well you should)

5. Now you should Give up and just download the jar file from our github type choco install make to install the windows version of make so you can run a makefile from PowerShell. (I know what you're thinking. "But there's a Windows subsystem for Linux that already has that". Once again, have a little faith. There's a reason for everything I tell you to do. Probably. But seriously, don't try to use the Linux subsytem to compile this project)

6. Now you'll need to make some changes to your Path. Go to your Windows start search bar and type "environment variables" and click on the top recommendation to edit your environment variables. Then click the "Environment Variables" button near the bottom. Add a new variable called JAVA_HOME and set it to C:\Program Files (x86)\Java\jdk1.8.0_241 (change it if your 32-bit java version is different, this is just the install location of whatever Java you installed). Then find you Path variable and edit it, to add %JAVA_HOME%\bin to your path. Do this for both user and system variables because I'm not sure which one actually matters it's clearly the only way to do this.

7. Now you can open a non-administrative Powershell (mostly just to feel safer about it, but also so it has your updated Environment Variable changes) and navigate to the cloned repository. Go to the src folder and type in Make.

8. Congratulations! You've successfully built our jar file! Now you can go install other stuff below.

Necessary Installs

1. Install Docker using the tutorial found here Note: If you're having trouble try py -3.6 -m pip install scbw to make it install scbw with that python version (3.6 in this case). Some machines may default to using the system python 2 so specifying which python to use for the install helped it work for me.

2. After you've installed docker, open a powershell window as administrator and run docker-machine create -d hyperv --hyperv-virtual-switch "Default Switch" default. We have no clue what it does, but it seems to work For the sake of Readme brevity, we will ommit the thorough explanation of the command which we had prepared (we're super experienced college undergrads tho, so you can obviously trust us to run commands as adminstrator with no explanations needed. The chocolately install thing from earlier didn't break your computer, so this will definitely finish it off won't either).

Running the Bot

1. Put the BotBoi3000.jar file that you either made or downloaded in the AI folder of the bots folder, which should be located at "Users/Your_User_Folder/AppData/Roaming/scbw/bots/". You will need to create a folder called BotBoi3000, as well as an AI folder, a Read folder, a Write folder, a json, and a BWAPI.dll file (follow the explanation in the Development section of this github page)

2. scbw.play --bots "BotBoi3000" "BotBoi3000" --show_all is to get it to play itself, and scbw.play --bots "BotBoi3000" --human --show_all is to get it to play against yourself.

Known limitations

1. It doesn't actually run if the bot's starting location is on the right or bottom side of the map. (Read as top left spawn location defines the current meta)
2. We don't actually have a Makefile, so don't try to make it. We now have a Makefile. Progress.

Strategy of Behavior Tree

We use two trees that repeatedly check conditions and execute every frame:

1. The first tree is for the build order and unit economy.
2. The second tree executes battle strategies.

For the Economy tree:

1. Root checks which type of enemy the bot is facing
2. The strategy routine/node/branch then implements a seqence of routines to act on
3. The sequence includes early, middle, and late game build actions. The sequence will continuously check status based on the number of units made versus the number of units able to be made. If any of the supply depots are destroyed, then the sequence will restart.

For the Strategy tree:

1. Root checks which type of enemy the bot is facing and which type of strategy to implement
2. The strategy routine/node/branch then implements a sequence of battle movements to act on
3. The sequence includes early scouting, moving mass amounts of troops for an attack, and a defend base warning routine when the enemy attacks

Current Functionality

Our tree skeleton is now functioning, and it is based on this implementation: https://www.javacodegeeks.com/2014/08/game-ai-an-introduction-to-behaviour-trees.html

Currently our economy tree is functioning, so our code uses a units made by units possible ratio to make decisions on what units to create. The economy is now fully dependent on this functionaility. It currently only works against Zerg opponents.

Also, now our strategy tree is functioning, so now our troop movement is fully dependent on this functionality as well.

Expert Knowledge

In terms of expert knowledge, we base our economy on a strategy that relies on our resource supply used and supply total values. This ensures that we strategically build the right things at the right time. We also implement scouting strategy to help us spot enemy buildings. Such information will help us make successful attacks against our enemy later in the game. Lastly, we have a survival strategies where we move our troops patrol/defend a building if it is our last one standing. This helps us do all we can to avoid a lose scenario when we are standing on our last leg.

Important Variables

A couple of important variables that impact bot behavior are supplyUsed(number of units alive) and supplyTotal(the number of units that can be made). The econonmy tree relies on these variables heavily to determine when it is appropriate to build something.

patrolCount is another important variable for defense purposes. This variable keeps track of how many troops have been sent to patrol a building, and the strategy tree uses it to determine whether to put more troops on patrol (as you don't want to use too many of your troops to patrol.

Features that can be added

Currently the bot's behavior is not dependent on the enemy species. Originally we envisioned our bot's behavior trees to consist of three subtrees (one for each possible species the bot would be facing). For the purposes of completing our class project, we figured it would make more sense to further develop quality behavior trees than attempt to make three mediocre subtrees for each different behavior. That is why our code only goes through the Zerg subtree regardless of the opponent race (the routines representing the starting point of the other subtrees have been commented out). Future contributors can build up the subtrees for the other races (terran and protoss) and modify the code in StratRoot.java and EconRoot.java so that the currect subtree is selected based on the opponent species. Contributor should look at how the Zerg subtrees were built up as guidance for how to build up the other subtrees.

Currently the bot doesn't vary too much in the types of troops it produces, but the blackboard is setup to accomodate creation of any type of terran troop. Contributors can add to bot by editing econ trees to create a more diverse set of troops (if doing so strategically would improve performance). Make sure you have the proper buildings with the proper upgrades to build the troops you see fit, using the bwapi build function. To give the troops you made specific directions, look to also editing the strat trees.

Known Bugs

We don't really have any known bugs (if something wasn't working we either fixed it or scrapped it from our bot). The gameplay screen does get messy with all the text that is written to it to show what bot tasks are being implemented, so a contributor could work on cleaning that up.