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Monthly Archives: December 2012

Since I spent some time tweaking the AI in Freekick 3, I thought I should write down some of the implementation details for future reference.

The AI has quite a simple general structure. Apart from the special cases like restarts, goalkeeper AI etc. the main gameplay AI consists of three states: defensive AI, offensive AI and the AI when the agent is about to kick the ball. Whether a player is in the defensive or offensive state depends on the match situation (where the ball is, who has the ball) and the team tactics (agent’s position).

The defensive state is basically a decision “where should I stand to make the opponent’s offensive difficult”. There are a few options: try to take the ball from the opponent, try to block an opponent’s pass or shot or guard some area or opponent. The AI simply assigns scores to each action and picks the action with the highest score. (This, I suppose pretty standard AI technique, seems to be inspired by utility functions and is used throughout the Freekick 3 AI.)

The offensive state is even simpler than the defensive state: either the player tries to fetch the ball or tries to place himself in the best possible supporting position, which should be somewhere that can be passed to, far away from opposing players, and a good shot position. (The AI builds a kind of an influence map that is also affected by some soccer-specific things such as the offside rule.)

Probably the most important decision the AI has to make is what to do when the agent has the ball. Again, like with the defensive state, the AI has a few different possible actions, and it assigns scores to all of them and simply picks the action with the highest score.

The possible actions are Pass, Shoot, Long Pass, Clear, Tackle and Dribble. I’ll start with the Pass action.

When deciding the pass target, the AI loops through all of the friendly players and keeps track of the best option. For each player that’s not too far or too close, the AI considers either passing directly to the player or trying a through-pass. The base score for the pass is highest for players nearer the enemy goal, and then decreased for each opponent player that is seen as a possible interceptor.

The Shoot action score is basically a function of distance to the opponent’s goal and the distance from the opponent’s players (especially the goal keeper) to the possible shot trajectory.

The Long Pass action is actually a composite of Shoot and Pass – it checks for the Shoot and Pass action scores of the friendly players and chooses to make a long pass (or cross) to the player with the highest score. As with other actions, the score is multiplied by a team tactics coefficient, allowing the coach to influence the team’s playing style.

Clear and Tackle are basically emergency brakes that the AI can pull in a situation where the ball needs to be kicked away from the own goal or the opposing players.

With Dribble, the AI creates a few possible rays at regular angle intervals around the agent as possible dribble vectors and assigns scores to each of them. Similar to shooting, the score is higher near the opponent goal, but decreased by opponent presence.

So, in the end, the AI is composed by several rather simple techniques. It’s all heuristics without any algorithms providing optimal solutions (if any can even be used in soccer AI). It uses some simple seeking behaviors (arrive at a position, chase the ball). There’s one simple state machine, with state transitions decided mostly by the ball position. The top level AI is built around simple if-then-else-statements (I suppose you could call them decision trees). Lots of the decision making uses some sort of fuzzy logic, even though it’s not really structured like that (instead the code itself is fuzzy). Still, the AI manages to seem smart in most cases, it plays rather well and presents a challenge for the human player (for me, at least).

There are still quite a few standard AI techniques that I haven’t implemented which might make sense for Freekick 3. For example it might be interesting to experiment with adding some kind of learning ability to the AI, which should be possible using reinforcement learning, or adding a more complicated planning process with the use of a decision network. A useful first step would be to extract all the used generic concepts like decision trees and fuzzy logic to their own code pieces, which would enable experimenting with things like learning a decision tree.

My conclusion is that there are lots of different game AI techniques, many of which are quite simple, and the key to creating a fun AI is to find out which techniques to use for which problem and how to combine the techniques. The techniques are often intuitive but can be also described mathematically, so that when reading up on game AI, you may, depending on the material, get an impression that game AI is either very non-scientific or very mathematical (and therefore difficult), while I think it can be either, depending on how you look at it.

For the interested, the ~500 lines of Freekick 3 AI that make up the core can be found at GitHub.

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It’s been a while since the last post.

This post is to announce a new version of Freekick 3, the latest rewrite of my project of writing a soccer game. It doesn’t have fancy graphics (on the contrary, it’s very ugly), but it tries to provide a fun soccer game to play nevertheless.

This is actually the first announcement of Freekick 3. The previous announcement on the Freekick series was the announcement of Freekick 2. Here’s a short overview of the features.

– A few different game modes, including friendly games, knockouts, leagues, seasons and a mockup of a career mode.

– The team, league and player data is not included, but can be imported to Freekick 3 either from Sensible World of Soccer data files or, my favourite, from Wikipedia. By having a script to fetch all the data from Wikipedia it’s ensured that the data is relatively up-to-date and that there’s a fair amount of teams to play against.

– The AI is improved and may provide a nice challenge.

– You can either control the whole team in the match or choose to just control one player.

You can download Freekick 3 (source) from https://github.com/anttisalonen/freekick3. I’ll show a quick tour around Freekick 3.

This is the main menu (quite similar to Freekick 2).

This is the main menu (quite similar to Freekick 2).

After fetching the data from Wikipedia you have lots of teams to choose from.

After fetching the data from Wikipedia you have lots of teams to choose from.

You can tune the line up before the match.

You can tune the line up before the match.

During a match - about to cross.

During a match – about to cross.

Career mode currently includes a league season along with the national cup.

Career mode includes a league season along with the national cup.

Football league two mid-season table.

Football league two mid-season table.

Fighting for the ball mid-field.

Fighting for the ball mid-field.