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AI and Machine Learning



So -  the last couple of weeks I have been working on building a framework for some AI.

In a game like the one I'm building, this is rather important. I estimate 40% of my time is gonna go into the AI.  What I want is a hunting game, where the AI learns from the players behaviour. This is actually what is gonna make the game fun to play.  This will require some learning from the creatures that the player hunt and some collective intelligence per species.  Since I am not going to spend oceans of Time creating dialogue, tons of cut-scenes and an epic story-line and multiple levels (I can't make something interesting enough to make it worth the time - I need more man-power for that), what I can do, is create some interesting AI and the feeling of being an actual hunter, that has to depend on analysis of the animals and experimentation on where to attack from.     SO.. To make it as generic as possible, I mediated everything, using as many interfaces a possible for the system.  You can see the general system here in the UML diagram. I customized it for Unity so that it is required to add all the scripts to GameObjects in the game world.   This gives a better overview, but requires some setup - not that bothersome. 

If you add some simple Game Objects and some colors, it could look like this in Unity3D: 






Now, this system works beautifully. The abstraction of the Animation Controller and Movement Controller assumes some standard stuff that applies for all creatures. For example that they all can move, have eating-, sleeping and drinking animations, and have a PathFinder script attached somewhere in the hierarchy.  It's very generic and easy to customize.  At some point I'll upload a video of the flocking behavior and general behavior of this creature.  For now, I'm gonna concentrate on finishing the Player model, creating a partitioned terrain for everything to exist in. Finally and equally important, I have to design a learning system for all the creatures. This will be integrated into the Brain of all the creatures, but I might separate the  collective intelligence between the species. 

It's taking shape, but I still have a lot of modelling to do, generating terrain and modelling/generating trees and vegetation. 

Thanks for reading,

UML AI.jpg


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I'd be glad to share my insight and experience with neural networked AI, including purely genetic / adaptive AI (which require no formal training data in order to improve themselves), but you know what? For most games, FSM are adequate, behaviour trees are awesome, you don't really need to get too carried away, and if you do, you can always add a bit more script.

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1 hour ago, leith said:

I'd be glad to share my insight and experience with neural networked AI, including purely genetic / adaptive AI (which require no formal training data in order to improve themselves), but you know what? For most games, FSM are adequate, behaviour trees are awesome, you don't really need to get too carried away, and if you do, you can always add a bit more script.

Wauw. Thanks. That's so nice of you. Neural networking is definitely the I'd like to go if this was intended to be just for scientific research. But since I am going to release it, I don't have the luxury of assuming multicore parallel hardware. 

I'm actually doing some research in evolution using neural networks/neural evolution and a custom real-time engine, written using directX and a custom physics engine. Very exciting stuff trying to emulate the real world and evolution of simple organisms, but it takes SO MUCH POWER. Real machine learning is not on the table. It's gonna be a simple model that can add states, actions and transition to the statemachine, correcting actions depending on whether the creature lives or dies when the player attacks. This makes the hunting dynamic. As soon as the player has assumed a hunting method that works, the creatures will adapt quickly after. This is of course still just hypothetical, since I've only just finished designing. 


I'd love to hear if you have specific ideas on how this could be implemented. I have some suggestions and models that I'm discussing with me and myself what would work best, but I'd like a fresh thought. I always enjoy fresh thoughts. 

Thanks for getting at me 🔥 

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This hunting game idea of yours sounds awesome.  I too am going to be incorporating strong elements of AI into the game I'm making.  Some of the concepts you've drafted up are similar such as the hierarchy of needs, though I don't think I'll be including many learned AI elements.  I'm looking forward to hearing about your project.  Are you coalescing the data into something which can be interpreted? or which is accessible to you the developer?  I'm just imaging that you might have an AI with runaway parts that don't serve your intentions, but this is all speculation. 

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On 7/2/2017 at 9:32 PM, Awoken said:

This hunting game idea of yours sounds awesome.  I too am going to be incorporating strong elements of AI into the game I'm making.  Some of the concepts you've drafted up are similar such as the hierarchy of needs, though I don't think I'll be including many learned AI elements.  I'm looking forward to hearing about your project.  Are you coalescing the data into something which can be interpreted? or which is accessible to you the developer?  I'm just imaging that you might have an AI with runaway parts that don't serve your intentions, but this is all speculation. 

Hi Awoken..  That's a very good question! Obviously it would make sense to output the generated behaviour patterns to a readable scheme or diagram, although this is not very easy. So far I have some algorithms to save the generated data, for what the creatures have learned, into file so that I can open them later. I'm still experimenting with the learning algorithms, trying to simplify the algorithm as much as possible. So I currently review some of the generated data in a simple graph viewer. For now that is okay, but it would be unreadable for anyone who didn't know the algorithm, and that's not ideal.   It's a work in progress. ;)

So far I want to make this pretty much as a Neural Network that can create new nodes from some simple parameters.  This neural network is saved as an xml-file whenever a generation survives better than the previous iteration. I have made a simple viewer for this format so I can keep track of what is working and what is not. This is still just experimental. A learning session would take  some time and hopefully produce some very interesting behaviour for the different creatures depending on their ability to move. I plan to take a generation that is half way to expert and make the players start with some AI that has the potential for learning when playing on console or PC, and then maybe make the player choose a difficulty when playing on and Android unit. 

I hope this is an answer to the degree of order I have on this. 

Edited by AlphaSilverback

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      On to the Constructor
      This state machine is still hard-coded. In order to generate the maze, we need to manually construct each room (aka FSM state, or vertex, or node). We also need to construct each transition (aka edge) of the state machine.
      public FunMachine() { // Create all the fun states in our mini-world FunMachineState entryHall = new FunMachineState("Grand Entrance", "You are standing in a grand entrance of a castle.\nThere are tables and chairs, but nothing you can interact with."); FunMachineState staircase = new FunMachineState("Grand Staircase", "The staircase is made from beautiful granite."); FunMachineState eastWing = new FunMachineState("East Wing", "This wing is devoted to bedrooms."); FunMachineState westWing = new FunMachineState("West Wing", "This wing is devoted to business."); FunMachineState bedroomA = new FunMachineState("Master Suite", "This is the master suite. What a fancy room."); FunMachineState bedroomB = new FunMachineState("Prince Bob's Room", "The prince has an extensive library on his wall.\nHe also has more clothes than most males know what to do with."); FunMachineState bedroomC = new FunMachineState("Princess Alice's Room", "The princess has filled her room with a small compur lab.\nShe spends her days playing games and writing code."); FunMachineState workroomA = new FunMachineState("Study", "This is the study. It has many books."); FunMachineState workroomB = new FunMachineState("Bathroom", "Every home needs one"); FunMachineState workroomC = new FunMachineState("Do Not Enter", "I warned you not to enter.\nYou are in a maze of twisty little passages, all alike."); FunMachineState passage = new FunMachineState("Twisty Passage", "You are in a maze of twisty little passages, all alike"); mExit = new FunMachineState("Outside", "You have successfully exited the castle."); // Hook up doors. entryHall.Neighbors.Add(staircase); entryHall.Neighbors.Add(mExit); staircase.Neighbors.Add(eastWing); staircase.Neighbors.Add(westWing); staircase.Neighbors.Add(entryHall); eastWing.Neighbors.Add(bedroomA); eastWing.Neighbors.Add(bedroomB); eastWing.Neighbors.Add(bedroomC); eastWing.Neighbors.Add(staircase); bedroomA.Neighbors.Add(eastWing); bedroomB.Neighbors.Add(eastWing); bedroomC.Neighbors.Add(eastWing); westWing.Neighbors.Add(workroomA); westWing.Neighbors.Add(workroomB); westWing.Neighbors.Add(workroomC); workroomA.Neighbors.Add(westWing); workroomB.Neighbors.Add(westWing); // Trap of doom. workroomC.Neighbors.Add(passage); passage.Neighbors.Add(passage); // Add them to the collection mStates = new List(); mStates.Add(entryHall); mStates.Add(staircase); mStates.Add(eastWing); mStates.Add(westWing); mStates.Add(bedroomA); mStates.Add(bedroomB); mStates.Add(bedroomC); mStates.Add(workroomA); mStates.Add(workroomB); mStates.Add(workroomC); mStates.Add(passage); mStates.Add(mExit); // Finally set my starting point mCurrent = entryHall; }  
      This creates a fun little graph. In case you're having trouble visualizing it, the picture version looks like this:

      It has rooms. It has a text adventure with a death trap room and a route to victory. It is starting to look like a real game.
      Run the Example Code
      If you haven't gone through the executable yet, now would be a great time to do it. You can explore this little text-based world. It doesn't have a plot, it doesn't have inventory items or any action other than moving, but it demonstrates a part of the game.
      Making it Data Driven
      That term shows up a lot in games: Data Driven.
      What does it mean? So far if I wanted to make changes I needed to modify the source code, recompile, and test the app. For a small castle map and a single developer, this is not a hard thing. But what happens when the app grows?
      Let's imagine I hire someone to design my levels. That person is not a programmer. I don't want them touching my C# files, and I don't want to teach them to read and write C#. So what should I do?
      Simple: I create a save file that contains all the information describing the level. I can allow non-programmers to work on my game by loading data at runtime. Data-driven means level designers can modify rooms, put different things in different locations, and otherwise improve the game without touching the code. It means another programmer can implement game objects like ropes and bottles and water without touching the game code. It means that if we were making a graphical game, artists could create new art, modellers could create new models, and animators could modify animations, all without touching the code. Data-driven means that only programmers need to touch the code. Everyone else uses data to modify the game. Fancy engines will implement ways to reload data while the game is running. Designers and artists and animators and modellers can iterate on their work much faster, potentially saving months of development time.
      Making My Dungeon Data Driven
      The states are only slightly modified from last time. Last time I stored the state's name, description, and neighbors.
      This time I add a unique name key and a set of flags. The flags indicate if the node is the Enter node (there is only one) or if the node is an exit node. Next I added the functions ReadXml and WriteXml. These two functions save and load my five elements (unique name, flags, visible name, description, and neighbors) into an XML file. Because it is basically free I chose to implement them using the IXmlSerializable interface. Someday when I'm feeling ambitious I can extend future components to also use the C# serialization routines to automatically handle my data. Since the state machine will need to create these objects from XML, I create a second constructor that takes an XmlReader and pass that on to the ReadXml function.
      Finally, I added some accessors and mutators (get and set functions) to help out the state machine.
      public class SavedMachineState : IState, IXmlSerializable { #region Members [Flags] public enum StateFlags { None = 0, Enter = 1, Exit = 2, } public string mKey; StateFlags mFlags; string mName; string mDescription; List mNeighbors = new List(); public List Neighbors { get { return mNeighbors; } } #endregion #region Constructors /// /// Manual constructor for default maze /// ///unique name for the stateFlags ///flags to indicate enter nodes and exit nodes ///name to show to the user ///text to show for the description ///unique keys for neighboring rooms, seperated by commas and not spaces public SavedMachineState(string uniqueKey, StateFlags flags, string name, string description, string neighbors) { mKey = uniqueKey; mFlags = flags; mName = name; mDescription = description; mNeighbors.AddRange(neighbors.Split(',')); } /// /// Constructor to create an object from a save file /// ///xml stream to read from public SavedMachineState(XmlReader reader) { ReadXml(reader); } #endregion #region Helper Functions public bool IsStartState { get { return (mFlags & StateFlags.Enter) != StateFlags.None; } } public bool IsExitState { get { return (mFlags & StateFlags.Exit) != StateFlags.None; } } public string Key { get { return mKey; } } public bool IsMyName(string nameToTest) { //TODO: Add shortcuts to names. For example, allow "Great Hall", "Hall", etc. if (nameToTest.ToLower() == mName.ToLower()) return true; if (nameToTest.ToLower() == mKey.ToLower()) return true; return false; } #endregion #region IState Overrides public override string GetName() { return mName; } public override void Run() { // We don't do any fancy stuff, just print out where we are Console.WriteLine(); Console.WriteLine(mDescription); } #endregion #region IXmlSerializable Members public System.Xml.Schema.XmlSchema GetSchema() { return null; } public void ReadXml(System.Xml.XmlReader reader) { reader.ReadStartElement(); mKey = reader.ReadElementContentAsString("UniqueName",""); string flagString = reader.ReadElementContentAsString("Flags",""); mFlags = (StateFlags)Enum.Parse(typeof(StateFlags), flagString); mName = reader.ReadElementContentAsString("VisibleName", ""); mDescription = reader.ReadElementContentAsString("Description", ""); string neighborsString = reader.ReadElementContentAsString("Neighbors", ""); mNeighbors.AddRange(neighborsString.Split(',')); reader.ReadEndElement(); } public void WriteXml(System.Xml.XmlWriter writer) { writer.WriteElementString("UniqueName", mKey); writer.WriteElementString("Flags", mFlags.ToString()); writer.WriteElementString("VisibleName", mName); writer.WriteElementString("Description", mDescription); string neighbors = String.Join(",",Neighbors.ToArray()); writer.WriteElementString("Neighbors",neighbors); } #endregion }  
      If that was the first exposure to the code it might be a little intimidating. But since we've been slowly adding to it over time, you should see that it is only a minor incremental change.
      State Machine Changes
      Changes to the state machine were a little more dramatic. The exit node information is now contained in the data, so I can drop the mExit state I mentioned earlier. For convenience, I moved the map construction code from a constructor to its own function: GenerateDefaultMap(). It allows us to generate and save a map when bootstrapping the toolchain. The constructor calls ImportFromXml() instead. If that fails we generate the default map, save a copy with ExportToXML(), and then reload our newly created map. ExportToXML() creates an XML writer, loops through the states, and writes each state out using the WriteXml() function. ImportFromXML() creates an XML reader and reads the file in through the corresponding ReadXml() function.
      Here's the modified code:
      public class SavedMachine : IStateMachine, IXmlSerializable { #region Members List mStates = new List(); SavedMachineState mCurrent; #endregion #region Constructor /// /// Initializes a new instance of the FunnerMachine class. /// public SavedMachine() { try { ImportFromXML(); } catch (Exception ex) { mStates.Clear(); } if (mStates.Count == 0) { GenerateDefaultMap(); ImportFromXML(); } // Find the entry state for (int i = 0; i < mStates.Count; i++) { if (mStates.IsStartState) { mCurrent = mStates; break; } } if (mCurrent == null) { Console.WriteLine("\n\nERROR! NO ENTRY STATE DEFINED."); throw new Exception("No entry state defined in this state machine. Cannot continue."); } } #endregion #region Helper Functions private void GenerateDefaultMap() { mStates.Clear(); // Create all the fun states in our mini-world mStates.Add(new SavedMachineState("entryHall", SavedMachineState.StateFlags.Enter, "Grand Entrance", "You are standing in a grand enterance of a castle.\nThere are tables and chairs, but nothing you can interact with.", "staircase,outside")); mStates.Add(new SavedMachineState("staircase", SavedMachineState.StateFlags.None, "Grand Staircase", "The staircase is made from beautiful granite.", "eastWing,westWing,entryHall")); mStates.Add(new SavedMachineState("eastWing", SavedMachineState.StateFlags.None, "East Wing", "This wing is devoted to bedrooms.", "bedroomA,bedroomB,bedroomC,staircase")); mStates.Add(new SavedMachineState("westWing", SavedMachineState.StateFlags.None, "West Wing", "This wing is devoted to business.", "workroomA,workroomB,workroomC")); mStates.Add(new SavedMachineState("bedroomA", SavedMachineState.StateFlags.None, "Master Suite", "This is the master suite. What a fancy room.", "eastWing")); mStates.Add(new SavedMachineState("bedroomB", SavedMachineState.StateFlags.None, "Prince Bob's Room", "The prince has an extensive library on his wall.\nHe also has more clothes than most males know what to do with.", "eastWing")); mStates.Add(new SavedMachineState("bedroomC", SavedMachineState.StateFlags.None, "Princess Alice's Room", "The princess has filled her room with a small compur lab.\nShe spends her days playing games and writing code.", "eastWing")); mStates.Add(new SavedMachineState("workroomA", SavedMachineState.StateFlags.None, "Study", "This is the study. It has many books.", "westWing")); mStates.Add(new SavedMachineState("workroomB", SavedMachineState.StateFlags.None, "Bathroom", "Every home needs one", "westWing")); mStates.Add(new SavedMachineState("workroomC", SavedMachineState.StateFlags.None, "Do Not Enter", "I warned you not to enter.\nYou are in a maze of twisty little passages, all alike.", "passage")); mStates.Add(new SavedMachineState("passage", SavedMachineState.StateFlags.None, "Twisty Passage", "You are in a maze of twisty little passages, all alike", "passage")); mStates.Add(new SavedMachineState("outside", SavedMachineState.StateFlags.Exit, "Outside", "You have successfully exited the castle.", "")); ExportToXML(); } public void ExportToXML() { XmlWriterSettings settings = new XmlWriterSettings(); settings.Indent = true; settings.OmitXmlDeclaration = true; settings.NewLineHandling = NewLineHandling.Entitize; using (XmlWriter writer = XmlWriter.Create("GameRooms.xml",settings)) { writer.WriteStartDocument(); writer.WriteStartElement("SavedMachine"); WriteXml(writer); writer.WriteEndElement(); writer.WriteEndDocument(); } } public void ImportFromXML() { XmlReaderSettings settings = new XmlReaderSettings(); settings.IgnoreWhitespace = true; XmlReader reader = XmlReader.Create("GameRooms.xml", settings); ReadXml(reader); } #endregion #region IStateMachine Overrides public override IState CurrentState { get { return mCurrent; } } public override string[] PossibleTransitions() { List result = new List(); foreach (string state in mCurrent.Neighbors) { result.Add(state); } return result.ToArray(); } public override bool Advance(string nextState) { foreach (SavedMachineState state in mStates) { if(state.IsMyName(nextState) && mCurrent.Neighbors.Contains(state.Key)) { mCurrent = state; return true; } } System.Console.WriteLine("Cannot do that."); return false; } public override bool IsComplete() { return mCurrent.IsExitState; } #endregion #region IXmlSerializable Members public System.Xml.Schema.XmlSchema GetSchema() { return null; } public void ReadXml(XmlReader reader) { bool isEmpty = reader.IsEmptyElement; reader.ReadStartElement(); if (isEmpty) return; while (reader.NodeType == XmlNodeType.Element) { if (reader.Name == "Room") { mStates.Add(new SavedMachineState(reader)); } else throw new XmlException("Unexpected node: " + reader.Name); } reader.ReadEndElement(); } public void WriteXml(XmlWriter writer) { foreach (SavedMachineState state in mStates) { writer.WriteStartElement("Room"); state.WriteXml(writer); writer.WriteEndElement(); } } #endregion } Not too bad for an incremental change.
      Run the Game and Generate Bootstrap Data
      Now when I run the game it attempts to load the save file. It cannot find one so it generates a new GameRooms.xml data file. Then it plays the same dungeon explorer code as before. Since I am bootstrapping my tools, I need to jump straight into the generated xml:
      entryHall Enter Grand Entrance You are standing in a grand enterance of a castle. There are tables and chairs, but nothing you can interact with. staircase,outside staircase None Grand Staircase The staircase is made from beautiful granite. eastWing,westWing,entryHall eastWing None East Wing This wing is devoted to bedrooms. bedroomA,bedroomB,bedroomC,staircase westWing None West Wing This wing is devoted to business. workroomA,workroomB,workroomC bedroomA None Master Suite This is the master suite. What a fancy room. eastWing bedroomB None Prince Bob's Room The prince has an extensive library on his wall. He also has more clothes than most males know what to do with. eastWing bedroomC None Princess Alice's Room The princess has filled her room with a small compur lab. She spends her days playing games and writing code. eastWing workroomA None Study This is the study. It has many books. westWing workroomB None Bathroom Every home needs one westWing workroomC None Do Not Enter I warned you not to enter. You are in a maze of twisty little passages, all alike. passage passage None Twisty Passage You are in a maze of twisty little passages, all alike passage outside Exit Outside You have successfully exited the castle.  
      I can look it over and verify that it is our original map, saved out in XML format. To prove that the system actually works, we can make some minor modifications to the map:
      This requires a tiny modification to the entrance hall (pointing the neighbor to 'courtyard') and making three new rooms. A few seconds in a text editor, copy/paste, a little wordsmithing, and I get this addition to the save file:
      courtyard None Courtyard The courtyard is decorated with many large trees and several marble benches. entryHall,townGate townGate None Town Gate You arrive at the gate of the town. Ahh, to be home again.\n\nNOTICE: The guards will not let you return to the castle if you leave. courtyard,village village Exit Quaint Village You return to your village. You won't soon forget your experiences in the castle. I fire up the game, and can quickly verify that I have altered the map just by changing data. Someone could now write a simple tool that allows a map designer to visualize dungeons. Or they could just let the map designer work with the raw XML files. Either way, the map is now data driven --- it can be modified entirely by data without any work from the programmer.
      Always Room for Improvement
      There are many more things we could do with this simple example. The first thing I would do is create objects that can be placed in rooms. These are simple state machines in themselves. For example, I could have a bucket object that has several states: Empty, Water, WaterAndFish. Events could be added (again with a simple state machine) that tracks your progress through a quest and grants points as you complete objectives. Those are nice things to have in a game, but in this case, they don't add anything beyond what was just demonstrated. That can be left as an exercise for the reader.
      A More Complex Set of Machines
      Let's move on to a more complex topic. Every game I've worked on has used an AI system of some form. There are actors and objects, and the actors do something. Often the actors use objects. For this demo, I created the following structure:

      The game container is a playing field. It contains a collection of GameObject instances. The playing field gets regular updates at about 30 frames per second, and each update gets passed along to the individual objects. There are two types of game objects: Pets and Toys. These to objects work together using activities. Now to go over each of these in detail.
      The Base GameObject Class
      A game object fits in with the state machines. They serve as both state machines AND as state nodes. Remember from earlier that a state machine is a concept. The implementation details don't matter when the concept is intact. It has an Update(), which means to run the current state, and also to advance the current state if necessary. We'll expand on this a little later. The GameObject represents any object we can place on our game board. They have an owner (in this case, the playing field). They have a location. They have an image. For convenience they have a ToString() override that makes things look nicer when I view them in a property grid. Also during development, they have evolved to have a PushToward() and a MaxSpeed() method. These would probably be integrated into a physics system or a collision system, but for now, this is their best natural fit.
      public abstract class GameObject { public PlayingField Owner { get; set; } /// /// Initializes a new instance of the GameObject class. /// public GameObject(PlayingField owner) { Owner = owner; } /// /// Update the object /// ///seconds since last update. /// seconds is the easiest scale for the individual settings public abstract void Update(float seconds); /// /// Location on the playing field to draw the actor /// public PointF Location { get; set; } /// /// What to draw on the playing field /// public abstract Image Image { get; } /// /// Push the game object toward a location. Default behavior is to not move. /// ///Location to push toward ///Seconds that have passed in this movement public virtual void PushToward(PointF destination, float seconds) { return; } /// /// Get the maximim speed of this game object. Default behavior is not to move. /// /// public virtual float MaxSpeed() { return 0; } /// /// Simplified name for the object to display in the property browser /// /// Shorter name public override string ToString() { string classname = base.ToString(); int index = classname.LastIndexOf('.'); string shortname = classname.Substring(index+1); return shortname; } }  
      All game objects need to implement the interface.
      Pets and Motives
      The basic pet class is pretty simple. A pet is a game object (so it gets everything above), plus it also gets an activity and a collection of motives. The motives are nothing more than a wrapper for the pet's status. In this case, we are only tracking fun and energy. (Note for comparison in The Sims3 there are 8 visible motives - hunger, social, bladder, hygiene, energy, and fun.) When a pet is created we default them to the Idle activity and initialize their Motives. We have a default update behavior to run whatever activity we are currently doing, or if we aren't doing anything to create a new idle activity and do that instead. We'll also implement what it means to push a pet.
      public class MotiveBase { public float Fun { get; set; } public float Energy { get; set; } } public abstract class Pet : GameObject { public MotiveBase Motives { get; set; } public Activities.Activity Activity { get; set; } /// /// Initializes a new instance of the Pet class. /// public Pet(PlayingField owner) : base(owner) { Activity = new Activities.Idle(this, null); Motives = new MotiveBase(); } /// /// Allow a pet to do something custom on their update /// /// protected virtual void OnUpdate(float seconds) { return; } public override void Update(float seconds) { if (Activity == null) { Activity = new Activities.Idle(this, null); } Activity.Update(seconds); } public override void PushToward(System.Drawing.PointF destination, float seconds) { base.PushToward(destination, seconds); // TODO: Someday accumulate force and make a physics system. Just bump it the correct direction. // TODO: Create a vector class someday float xDiff = destination.X - Location.X; float yDiff = destination.Y - Location.Y; float magnitude = (float)Math.Sqrt(xDiff * xDiff) + (float)Math.Sqrt(yDiff * yDiff); if (magnitude > (MaxSpeed() * seconds)) { float scale = (MaxSpeed() * seconds) / magnitude; xDiff *= scale; yDiff *= scale; } Location = new PointF(xDiff + Location.X, yDiff + Location.Y); } } There are a few TODO: markers in the code, but the interface work. Remember the Dependency Inversion Principle mentioned before: Program against an interface or abstract base class, don't program against the individual concrete classes.
      Finally, we get to create a concrete class. A puppy. Note that we're just pulling the values from saved resources so a designer and artist can modify them later.
      class Puppy : Pet { /// /// Initializes a new instance of the GameObject class. /// public Puppy(PlayingField owner) : base(owner) { } public override System.Drawing.Image Image { get { return FSM_Puppies.Properties.Resources.Puppy; } } public override float MaxSpeed() { return FSM_Puppies.Properties.Settings.Default.Pet_Puppy_MaxSpeed; } } Yup, all that work and we only get a tiny little concrete class. That is actually a very good thing. It means that when we want to extend it later for different kinds of puppies, kitties, horses, and other pets, we only need to add a tiny bit of code to hook up the new actors with their new data.
      A toy is also a game object, so it can behave as a state machine and as a state node, as appropriate. A toy has a default activity associated with it. When a pet attempts to use a toy they will get this default activity (aka behavior tree) and start running it. A toy is also responsible for computing the interest level in the object. For now, these will just be hard-coded formulas inside each toy object. Later on, these could be a more complex series of interactions but for this system it is adequate. Here's the Toy abstract class:
      public abstract class Toy : GameObject { /// /// Initializes a new instance of the Toy class. /// public Toy(PlayingField owner) : base(owner) { } public abstract Activities.Activity DefaultActivity(Pets.Pet actor, GameObject target); public abstract float Interest(Pets.Pet pet); public override void Update(float seconds) { // Note that most toys do nothing of themselves. They are driven by their activities. return; } }  
      Two Toys
      Now we'll create two concrete classes for toys. First, a sleeping mat. The interest of the sleeping mat is only based on energy. It has an image to draw. The default activity is to sleep on the mat.
      class SleepingMat : Toy { /// /// Initializes a new instance of the SleepingMat class. /// public SleepingMat(PlayingField owner) : base(owner) { } public override FSM_Puppies.Game.Activities.Activity DefaultActivity(Pets.Pet actor, GameObject target) { return new Activities.SleepOnMat(actor, this); } public override System.Drawing.Image Image { get { return FSM_Puppies.Properties.Resources.SleepingMat; } } public override float Interest(FSM_Puppies.Game.Pets.Pet pet) { return MaxEnergy() - pet.Motives.Energy; } }  
      Second, a ball to kick around. The interest is only based on fun, although it probably should include an energy component. It has an image to draw, and the default activity is to chase the ball.
      class Ball : Toy { /// /// Initializes a new instance of the Ball class. /// public Ball(PlayingField owner) : base(owner) { } public override Image Image { get { return FSM_Puppies.Properties.Resources.Ball; } } public override Activities.Activity DefaultActivity(Pets.Pet actor, GameObject target) { return new Activities.ChaseBall(actor, target); } public override float Interest(FSM_Puppies.Game.Pets.Pet pet) { return MaxFun() - pet.Motives.Fun; } } Now we move on to the activities that drive the system.
      Activities Are Both Glue and Oil
      Activities serve as glue to the system. They are the interactions between actors and objects. Without them there wouldn't be much of a connection between the two. Activities also serve as the oil that keeps the parts moving smoothly. They are constantly moving. They change themselves, and they change the actors they work with, and they can change the objects they work with. A more complex example of a food bowl could change the actor by modifying hunger, and also change the target by reducing the amount of food in the bowl.
      So here is our activity base class. An activity has an Actor and a Target. I intentionally limited Actors to be pets. I could have allowed any object to interact with any object, but that doesn't quite make sense in practice. We don't really want a food bowl to interact with a chew toy, or a ball to interact with a sleeping mat. We DO want to allow a pet to be a target allowing default activities to play social events. For example, pets could dance together or sniff each other or do whatever groups of pets do together. We allow an Update event on the activity base. This update is run by the pet earlier. We pass that on through the OnUpdate callback in each activity. If the activity returns true then we know it is complete and the pet needs to find something new to do.
      Finally, we have a magical function, FindBestActivity() that needs to live somewhere in the code. I could have created another class for it, but for now, this is the best spot.  This FindBestActivity is the magic that makes the AI do fun things. In this example, it is only 35 lines. We loop over all the toys in the game world and see how interesting they are. Then we take the best interaction and return a new instance of it. If we fail we just return the idle activity. For a game like The Sims, there are potentially tens of thousands of objects to choose from, and each object can have many activities associated with it. Finding the best activity among them all is a complex job. The theory behind it is no different: Find the best activity, and create an instance of it.
      public abstract class Activity { public Pets.Pet Actor { get; set; } public GameObject Target { get; set; } /// /// Initializes a new instance of the Activity class. /// public Activity(Pets.Pet actor, GameObject target) { Actor = actor; Target = target; } /// /// Update this activity state /// /// elapsed time /// true if the activity is complete public abstract bool OnUpdate( float seconds ); /// /// Update this activity state /// /// elapsed time public void Update(float seconds) { if(OnUpdate(seconds)) { Actor.Activity = new Idle(Actor, null); } } /// /// Utility function to locate the best next activity for the actor. /// /// public static Activity FindBestActivity(Pets.Pet actor) { // Look for a toy to play with... if (actor.Owner != null && actor.Owner.GameObjects != null) { List candidates = new List(); foreach (GameObject obj in actor.Owner.GameObjects) { Toys.Toy t = obj as Toys.Toy; if (t != null) { candidates.Add(t); } } if (candidates.Count > 0) { float bestScore = float.MinValue; Toys.Toy bestToy = null; foreach (Toys.Toy t in candidates) { float myscore = t.Interest(actor); if(myscore>bestScore) { bestScore = myscore; bestToy = t; } } return bestToy.DefaultActivity(actor, bestToy); } } return new Idle(actor, null); } public override string ToString() { string classname = base.ToString(); int index = classname.LastIndexOf('.'); string shortname = classname.Substring(index + 1); return shortname; } } Now we know what an activity is. Basically just another state machine.
      Idle Activity
      We'll start with the idle activity. It has an idle time. After enough time has passed we look for something new to do. This new activity will replace our current idle activity. If we don't find anything interesting to do we can just sit there, slowly dropping our fun and our energy. Since this is C# we don't need to schedule cleanup and deletion of our own idle activity which simplifies our code quite a lot.
      class Idle : Activity { float mTimeInIdle = 0; public Idle(Pets.Pet actor, GameObject target) : base(actor, target) { } public override bool OnUpdate(float seconds) { mTimeInIdle += seconds; if (mTimeInIdle >= FSM_Puppies.Properties.Settings.Default.Activity_Idle_WaitingTime) { Actor.Activity = FindBestActivity(Actor); } // Sitting there idle isn't much fun and slowly decays energy. This encourages us to pick up other activiites. Actor.Motives.Fun += FSM_Puppies.Properties.Settings.Default.Activity_Idle_Fun * seconds; Actor.Motives.Energy += FSM_Puppies.Properties.Settings.Default.Activity_Idle_Energy * seconds; // Always return false because idle is never finished. It auto-replaces if it can find something. return false; } }  
      ChaseBall Activity
      This is actually TWO activities. It is a miniature state machine within itself. Chasing a ball has one component "RunToObject", and then a second component where they actually kick the ball. It isn't a difficult state machine, just two states, and can be represented directly with a simple if statement. That is a good time for this reminder:
      So every update we attempt to run to the ball object. If we succeeded in running to the object, we kick the ball a random distance. We also bump fun a little bit whenever they kick the ball. An activity's return result indicates when it is complete. We'll return true only when our fun is maxed out. We might want to have a second exit condition when energy runs low, but that is for later.
      class ChaseBall : Activity { RunToObject mRto; /// /// Initializes a new instance of the ChaseBall class. /// public ChaseBall(Pets.Pet actor, GameObject target) : base(actor, target) { mRto = new RunToObject(actor, target); } public override bool OnUpdate(float seconds) { // When they kick the ball, move it to a new location and continue our activity. if( mRto.OnUpdate(seconds)) { float kickDistance = FSM_Puppies.Properties.Settings.Default.Activity_ChaseBall_KickDistance; // Get a random number with +/- kick distance float newX = Target.Location.X + (((float)Target.Owner.Rng.NextDouble()*(2*kickDistance))-kickDistance); float newY = Target.Location.Y + (((float)Target.Owner.Rng.NextDouble()*(2*kickDistance))-kickDistance); PointF randomLocation = new PointF(newX,newY); Target.Location = randomLocation; Actor.Motives.Fun += FSM_Puppies.Properties.Settings.Default.Toy_Ball_Fun; if(Actor.Motives.Fun > 100) return true; } return false; } }  
      RunToObject Activity
      Next, we'll look at how they run to an object. It is pretty simple. If we are close enough (another designer-adjustable value) then they have made it to the object and we return true. If they are not there yet we push them toward the object, drop their energy, and return false (we aren't done with the activity yet).
      class RunToObject : Activity { /// /// Initializes a new instance of the RunToObject class. /// public RunToObject(Pets.Pet actor, GameObject target) : base(actor, target) { } public override bool OnUpdate(float seconds) { // Are we there yet? // And why didn't PointF implement operator-() ? PointF offset = new PointF( Target.Location.X - Actor.Location.X, Target.Location.Y - Actor.Location.Y); float distanceSquared = offset.X * offset.X + offset.Y * offset.Y; float closeEnough = FSM_Puppies.Properties.Settings.Default.Activity_RunToObject_CloseEnough; float closeEnoughSquared = closeEnough * closeEnough; if (distanceSquared < closeEnoughSquared) return true; Actor.PushToward(Target.Location, seconds); Actor.Motives.Energy += FSM_Puppies.Properties.Settings.Default.Activity_RunToObject_Energy * seconds; return false; } }  
      Sleeping On the Mat
      Just like chasing a ball, we start out by running to the object. So first we call RunToObject. If it succeeds (meaning we finally got there), then we start resting. We bump the motives, return true or false based on our energy status.
      class SleepOnMat : Activity { RunToObject mRto; /// /// Initializes a new instance of the SleepOnMat class. /// public SleepOnMat(Pets.Pet actor, GameObject target) : base(actor, target) { mRto = new RunToObject(actor, target); } public override bool OnUpdate(float seconds) { // Route to the sleeping mat if(mRto.OnUpdate(seconds)) { // Now that we are on the mat, just sit here and increase our motives. Actor.Motives.Energy += FSM_Puppies.Properties.Settings.Default.Toy_SleepingMat_Energy; Actor.Motives.Fun += FSM_Puppies.Properties.Settings.Default.Toy_SleepingMat_Fun; if (Actor.Motives.Energy > 100) return true; } return false; } }  
      Proof of Concept is Complete
      And... we're done. Go ahead and play with the sample now. Drop multiple play mats, multiple balls, and multiple puppies. Watch them run around chasing balls, and when they get tired watch them run over and sleep on the mat. Pick them from the list on the left-hand pane to view details in the little property grid. It is not much, but it is enough to see a sandbox game starting to grow. I showed my children and they immediately made a list of new objects to add. Add a kitty! Add a teeter-totter! Add a tetherball! (Really?) Repeat this around thirty times with new objects, add a few motives, and you can have your own self-running simulated pet world. Focus on a few of them to create minigames, and drop some spawn points in the world. Repeat until the game is complete.
      But I Wanted a First Person Shooter
      That is solved very easily: Rename "Pets" to "Monsters", and "Puppy" to "Grunt". Rename "Toys" to "WayPoint", "Ball" to "Flamethrower", and "SleepingMat" to "SpawnPoint". Finally, rename activities as appropriate.
      Wrapping It Up
      So we learned that state machines are the least powerful of the automata in computer science. We also learned that they are used everywhere in our daily lives, and can do incredible things for games. We can use state machines to run different code based on state. We can use state machines to represent arbitrary meshes. These meshes can include things like maps and connectivity grids. We don't need to hard code the state machines. When you load them from data you can get substantially more use out of them. Designers and artists and modellers and animators and producers can all modify the game without touching the code. We can build complex systems such as AI behavior trees out of a nested tree of very simple state machines.
      How Else Can I Apply This
      Most games apply these concepts all over the place. One of the most common jobs in the industry is Gameplay Engineer, or GPE. All they do is create the exact types of items we have made in this series of tutorials.
      For example, in Littlest PetShop I spent months adding new behaviors and actions. They ranged from 'in the world' behaviors like those above to minigames such as hide and seek where pets would run behind bushes to hide, then run up to the camera and celebrate with the player when they were found. In The Sims every one of the tens of thousands of objects in the game required scripting. You need a pile of clothes, then you need some of these scripts: Behaviors and interactions to create the piles of clothes; interactions to drop the piles of clothes and pick them up again; interactions for a washing machine; interactions for a dryer; interactions for the maid to come clean up; and on and on and on. Or if you are into faced paced games, you need behaviors and actions for your main characters to know to attack or run away, or decide if they should attack towers or players or minions. You need to create objects to pick up, weapons, inventories, and more. All of these behave the same way that we demonstrated above. It uses a common interface for objects, and the programmer fills out the details. These little activities and behaviors grow into complex ecosystems powering most games.
      So there you go. A text-based dungeon crawler prototype and a sandbox world prototype, both filled with state machines and written in just a few hours. Creating objects and behaviors like this describes the daily life of a gameplay engineer. Chances are good that if you get a programming career in the industry you will spend a few years of your life writing code much like that above. Remembering that it is a simple state machine can help avoid many nasty bugs and can simplify your life greatly.
      Thanks for reading.
      Source code can be found here: StateMachineTutorialsV5.zip

      Updates: 2013-03-26 Fix a typo in the map images.
      [Wayback Machine Archive]
    • By ShadowDurza
      REF URL: http://www.scp-wiki.net/
      I know that there are a few SCP-based fan games such as Containment Breach, but I feel that they don't really capture the true essence of the SCP stories. So I had an idea about a game that could really do so. Granted, this is purely conceptional and I neither have the skills nor the means to actually make this on my own.
      Basically, it's like a cross between FNAF and the kind of game category that SIMULACRA and Don't Chat With Strangers fit into. You are a detainee at an SCP Foundation site, why you're there or even how you got there is a complete mystery to you. The Foundation provided you with a PC with a limited internet connection presumably to keep you occupied, but by some divine prank, there also happened to be an experimental web browser installed in said PC that allows you to access information about the Foundation and SCPs along with a direct line of communication to certain members along with a background and psychological file for each one with no chance of being traced back to you.
      In the beginning, you only have level 0 clearance and the level of influence of a C class personnel but by communicating with and gaining the trust of personnel they will give you higher levels of clearance which will give you access to more information and can even be given the authority of certain staff members such as researchers, (which will allow you to administer tests) or a security officer (which will allow you to raise or lower security in certain sites or areas). However, each action comes with a degree of risk and the results of certain actions cannot be undone. To help you through this, you also have an experimental semi-sapient A.I. installed on the PC that, using the information you've managed to unlock, is able to calculate the odds of success for each action or attempt at communication. Once you get the right clearance, you are able to cause things to happen that will allow you to manipulate the odds of success for other actions and attempts to get higher clearance, like if a certain personnel has a flirting communication option but only likes a specific gender you can use SCP-113 in order to swap to the preferred gender in order to increase the likelihood of success for the attempt.
      Perhaps the riskiest action one can do is to directly interact with Keter-class SCPs and as such the success of the action can lead to a massive reward, if the player has completed the right actions before doing so then they may have little to no risk of failure or a critical failure. A critical failure would be the result of an action that would get the player character killed. After which the only option left is to start over from the beginning. However, each time the player starts a new game everything about the character gets randomized. This can range from things like the physical characteristics of the player, some basic background information, the characters name and age, and even which site the player is housed in. This is important because certain SCPs only react under specific conditions, like whether or not it's the player character's birthday or even if the character has a particular name. It can also result in life-or-death situations like if there is a containment breach and if the player character is in the same site as a highly dangerous SCP it may end up finding and killing them. Or if the situation on a site gets so bad that they have to detonate an on-site nuke.
      Eventually, in some way or another, the player will discover that their only hope of regaining their freedom is to communicate directly to the mysterious Administrator of the entire Foundation. You could consider this point the endgame because it would require to confirm the identities of certain personnel who claim the identities of members of the O5 Council or the Administrator. Your best option is to utilize the most classified information you can find and use it to make each possible Administrator or Council Member tell what they know about them. From then you have to look for inconsistencies in each unknown personnel's stories in order to find the one out of them that knows things that the others don't. Once you've decided who the Administrator is they will give you instructions to follow that will result in two undeterminable outcomes. If you chose right, you win and go free. If you chose wrong, it means instant death.
      Like my idea, want to add to it, or want to make it happen? Please, Leave a reply!

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