The table is just a visual aid — the procedure requires only that you multiply out the probabilities at each step. This means you could look a long way into the future, with one significant caveat: we are making an assumption that the chance of entering a room depends entirely on the current room they are in. This is what we call the Markov Property — the idea that a future state depends only on the present state.
While it allows us to use powerful tools like this Markov Chain, it is usually only an approximation. What about our fighting game combo-spotting example?
This is a similar situation, where we want to predict a future state based on the past state in order to decide how to block or evade an attack , but rather than looking at a single state or event, we want to look for sequences of events that make up a combo move.
One way to do this is to store each input such as Kick , Punch , or Block in a buffer and record the whole buffer as the event.
It would be possible to look at all the times that the player chose Kick followed by another Kick in the past, and then notice that the next input is always Punch. This lets the AI agent make a prediction that if the player has just chosen Kick followed by Kick, they are likely to choose Punch next, thereby triggering the SuperDeathFist.
This allows the AI to consider picking an action that counteracts that, such as a block or evasive action. These sequences of events are known as N-grams where N is the number of items stored. In the previous example it was a 3-gram, also known as a trigram, which means the first 2 entries are used to predict the 3rd one. In a 5-gram, the first 4 entries would hopefully predict the 5th, and so on. Lower numbers require less memory, as there are fewer possible permutations, but they store less history and therefore lose context.
On the other hand, higher numbers require more memory and are likely to be harder to train, as you will have many more possible permutations and therefore you might never see the same one twice. For example, if you had the 3 possible inputs of Kick , Punch , or Block and were using grams then you have almost 60, different permutations.
Tri-grams and larger N-grams can also be thought of as Markov Chains, where all but the last item in the N-gram together form the first state and the last item is the second state. Our fighting game example is representing the chance of moving from the Kick then Kick state to the Kick then Punch state. By treating multiple entries of input history as a single unit, we are essentially transforming the input sequence into one piece of state, which gives us the Markov Property — allowing us to use Markov Chains to predict the next input, and thus to guess which combo move is coming next.
But how do we observe a whole game world effectively? We saw earlier that the way we represent the geography of the world can have a big effect on how we navigate it, so it is easy to imagine that this holds true about other aspects of game AI as well. How do we gather and organise all the information we need in a way that performs well so it can be updated often and used by many agents and is practical so that the information is easy to use with our decision-making?
How do we turn mere data into information or knowledge? This will vary from game to game, but there are a few common approaches that are widely used. Sometimes we already have a ton of usable data at our disposal and all we need is a good way to categorise and search through it. For example, maybe there are lots of objects in the game world and some of them make for good cover to avoid getting shot. Or maybe we have a bunch of voice-acted lines, all only appropriate in certain situations, and we want a way to quickly know which is which.
The obvious approach is to attach a small piece of extra information that we can use in the searches, and these are called tags. Take the cover example; a game world may have a ton of props — crates, barrels, clumps of grass, wire fences. Some of them are suitable as cover, such as the crates and barrels, and some of them are not, such as the grass and the wire fence.
Once they do this for all your barrels and intact crates, your AI routine only has to search for any object with that tag, and it can know that it is suitable. This tag will still work if objects get renamed later, and can be added to future objects without requiring any extra code changes.
Some engines provide tag functionality built in, such as Unity and Unreal Engine 4 so all you have to do is decide on your set of tags and use them where necessary. Imagine a medieval city simulator, where the adventurers within wander around of their own accord, training, fighting, and resting as necessary. That works.
You find yourself needing to support multiple tags per location, looking up different animations based on which aspect of the training the adventurer needs, etc. It can then move to the relevant place, perform the relevant animation or any other prerequisite activity as specified by the object, and gain the rewards accordingly. An archer character in the vicinity of the above 4 locations would be given these 6 options, of which 4 are irrelevant as the character is neither a sword user nor a magic user.
By matching on the outcome, in this case, the improvement in skill, rather than on a name or a tag, we make it easy to extend our world with new behaviour. We can add Inns for rest and food. We could allow adventurers to go to the Library and read about spells, but also about advanced archery techniques. This system gives us more flexibility by moving these associations into data and storing the data in the world.
By having the objects or locations — like the Library, the Inn, or the training schools — tell us what services they offer, and what the character must do to obtain them, we get to use a small number of animations and simple descriptions of the outcome to enable a vast number of interesting behaviours.
Instead of objects passively waiting to be queried, those objects can instead give out a lot of information about what they can be used for, and how to use them.
Often you have a situation where part of the world state can be measured as a continuous value — for example:. You also might have some aspect of your AI system that requires continuous-valued inputs in some other range.
Similarly, while the health percentage value is broadly linear, the distance is not — the difference between an enemy being m away and m away is much less significant than the difference between an enemy 10m away and one right in front of the character.
Ideally, we want an approach that can take these 2 measurements and convert them into similar ranges so that they can be directly compared. And we want designers to be able to choose how these conversions work, so that they can control the relative importance of each value. Response Curves are a tool to do just that. The line or curve through the graph determines the mapping from the input to the normalized output, and designers tweak those lines to get the behavior they want.
This better reflects the urgency that applies as an enemy draws nearer. With these 2 values both scaled into the 0 to 1 range, we could calculate the overall Safety value as the average of the two input values. Often we find ourselves in a situation where the AI for an agent needs to start keeping track of knowledge and information it is picking up during play so that it can be used in future decision making.
Maybe an agent needs to remember who the last character to attack it was, so that it knows that should be the focus of attacks in the short term. Or maybe it wants to note how long it was since it heard a disturbance, so that after some period of time it can stop investigating and go back to whatever it was doing before. Often the system that writes the data is quite separate from the system that reads the data, so it needs to be easily accessible from the agent rather than built in to the various AI systems directly.
The reads may happen some time after the writes, so it needs to be stored somewhere that it can be retrieved later rather than being calculated on demand, which may not be possible. In a hard-coded AI system the answer here is usually just to add the necessary variables as the need for them arises. These variables go into the character or agent instances, either directly inline, or in the form of a separate structure or class to hold this information. AI routines are adapted to read and write from this data as needed.
This works well as a simple approach, but can get unwieldy as more and more pieces of information need adding, and usually requires rebuilding the game each time. A more advanced approach might be to change this data store into something that allows systems to read and write arbitrary data — something that would allow new variables to be added without needing to change the data structure, and thereby increasing the number of changes that can be made from data files and scripts without needing a rebuild.
In traditional AI the emphasis is usually on collaboration between numerous systems to jointly solve a problem, but in game AI there are relatively few systems at work. Still, some degree of collaboration may still take place. Imagine the following in an action RPG:. A lot of this data may look redundant — after all, it should be easy to derive the distance to the nearest enemy whenever it is needed simply by knowing who that enemy is and querying for their position.
But that is potentially a slow operation if done many times a frame in order to decide whether the agent is threatened or not — especially if we also need to repeat the spatial query to find out which enemy is closest.
Unreal Engine 4 uses a dynamic blackboard system for the data provided to its Behaviour Trees. By providing this shared data object it is easy for designers to write new values into the blackboard based on their Blueprints visual scripts and for the behaviour tree to read those values later to help choose behaviour, all without requiring any recompilation of the engine.
A common problem in game AI is deciding exactly where an agent should try and move to. Our game is likely to have all that data to hand, but making sense of it is tricky.
We need a way to take the local area into account to give us a better overview of the situation. The Influence Map is a data structure designed to do exactly this. In implementation terms, we approximate the game world by overlaying a 2D grid, and after determining which grid square an entity is in, we can apply their influence score — representing whatever aspect of the gameplay we are trying to model — to that square and some of the surrounding ones.
We accumulate these values in the same grid to gain the overall picture. Then we can query the grid in various ways to understand the world and make decisions about positioning and destinations. We have a defensive wall that we want to send footsoldiers to attack, but there are with 3 catapults behind it — 2 close together on the left, and 1 over on the right.
How do we choose a good position for the attack? Plotting these scores on the influence map for one turret looks like this:. The blue box covers all the squares that we might consider attacking the wall.
Now we have a full indication of the area covered by the catapults. The benefit of the influence map here is that it transforms a continuous space with an almost endless set of position possibilities into a discrete set of rough positions that we can reason about very quickly. Still, we can get that benefit just by picking a small number of candidate attack positions - why would we choose to use an influence map here instead of manually checking the distance to each turret for each of those positions?
Firstly, the influence map can be very cheap to calculate. Secondly, we can overlay and combine multiple influence maps to perform more complex queries. For example, to find a safe place to flee to, we might take the influence map of our enemies, and subtract the map of our friends — gridsquares with a high negative score would therefore be considered safe. More red means more danger, more green means more safety.
Areas where they overlap can fully or partially cancel out, to reflect the conflicting areas of influence. Finally, they are easy to visualise by drawing them in the world. This can be a valuable aid to designers who need to tune the AI based on visible properties, and can be watched in real-time to understand why the AI is making the decisions that it does.
Hopefully this has given you a broad overview of some of the most common tools and approaches used in game AI, and the situations in which they are useful. Many other techniques - less commonly used but potentially just as effective - have not been covered, and these include:.
To read more on these topics, and the topics covered in detail above, the following sources may prove useful. Of course, on GameDev. Beyond that there are several good books on general game AI written by industry pros and it would be hard to single any out above the others - read the reviews and pick one that sounds like it would suit you.
Did you like this tutorial? You might also like these other tutorials from GameDev. Very comprehensive and informative article.
One thing though, the Monte Carlo link takes me to ebay search for listings of Monte Carlo vehicles. This is an amazing overview! I'd just like to comment on one point regarding the use of neural networks:.
The statement definitely rings true for supervised learning techniques usefdfor training neural nets. However, the late advancements in reinforcement learning could make the use of neural networks feasible also in commercial gaming. Differently from supervised learning, reinforcement learning enables developers to train a neural network without the need of training data. In a reinforcement learning setting, a network can learn by trial and error by running a number of simulations of a game and being exposed to a system of rewards.
This approach has been proven to be very successful for developing AI agents that play Atari games, Go and Chess thanks to the work carried out at Deep Mind. Several techniques have been proposed to train networks with reinforcement learning, such as Q-Learning and the use of Genetic Algorithms as a way of evolving the weights and hyper-parameters of a training network.
This is definitely exciting research that I'd love to see included in commercial games at some point! This is definitely an interesting area of research, but it is currently focused on playing existing games rather than creating AI for a new game. Get started for free!
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This tutorial is an excellent starting point for those looking to learn GameMaker Studio and create their own arcade games. Follow along with the instructor from Ask Gamedev to learn the ins and outs of GameMaker including how to use sprites, objects, events, and action blocks.
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