To push the boundaries of technology, the world cup football for robots, RoboCup, is organized on a yearly basis since 1997. To push the boundaries of artificial intelligence, a simulated version of the RoboCup, AI World Cup Football, is arranged yearly from 2017. This requires skillful attackers, defenders and goalkeeper. A large part of having a competent goalkeeper, is having a well-anticipating goalkeeper. This research will improve the goalkeeper’s level of anticipation by finding its own weak spots. By training an attacker against this goalkeeper, the weak spots can be determined. Based on this data the goalkeeper can estimate in which region of the goal a real opponent is likely to score and decrease that scoring chance by making a move. Heuristics and a deep neural network are used to estimate how likely the attacker is to make a goal. The results show that the attacker that uses the deep neural network has a chance of scoring a goal which is about 10 to 15 percentage points higher than a random shooting attacker. The deep neural network attacker is however outperformed by the heuristic based player by about 15 to 20 percentage points. Taking that into account, embedding the heuristic based attacker into the goalkeeper gives the goalkeeper a good sense of what areas of the goal it should cover to decrease the likelihood of conceding a goal.

Virtual robot soccer competitions have been rising in popularity in recent years, due to their easier accessibility than physical robot soccer. This is also the reason these virtual competitions were created. The goal is to compete in the AI world cup, one of the virtual soccer competitions. A coach-based framework has already been built, and the aim is to create strong defensive capabilities for the robot team. This paper in particular focuses on covering opponents without the ball. The marking strategy and specific opponent covering that have been devised for this are explained. Both are completely rule-based, and opponent covering has been thoroughly tested with an in-house developed batch-runner. In short, opponent covering consists of standing at the optimal position between the passer and receiver, and when the ball is passed going to the closest intercept point. This optimal position is learned with the batch-runner. Opponent covering performs well in the generated test cases. Some specific algorithms counter attacking strategies which have not been implemented yet, therefore it was not possible to evaluate those algorithms. They are theoretically optimal however.

Robot soccer competitions have been around for a while and have been a great environment to develop AI algorithms in. One of these environments is the AI world cup. The AI world cup environment is a virtual environment where two teams with five robots each play a soccer match. This paper focuses on defending the attacker that is carrying the ball in the AI world cup environment. This is achieved by comparing two approaches, a rule-based algorithm, and a Deep Deterministic Policy Gradient rein- forcement learning algorithm. The two algorithms are based on a coaching framework build by the TU Delft. This framework implements the actions of the robots, such as moving, jumping, and kicking the ball, and the communication between the robots. The two approaches have been evaluated and the rule-based outperforms the reinforce- ment learning algorithm. Furthermore, a teamwork strategy between the goalkeeper and defender has been developed, where this teamwork is a significant improvement over the non-teamwork algorithm.

The AI World Cup is a virtual competition in which teams of five players compete in a football match. The defensive strategies for the goalkeeper in this environment are yet to be researched, however. In previous editions of the competition the participating teams use a basic goalkeeper that can only dive but not position itself. This project provides research into the use of different positioning techniques to improve the performance of the goalkeeper.
In the project two different positioning techniques are presented and evaluated. In the first technique, the goalkeeper moves on an imaginary arc in front of the goal and in the second technique the goalkeeper moves on a straight line in front of the goal. Moreover, teamworking techniques between a defender and the goalkeeper are used to create a better coverage of the goal. The evaluations for the positioning techniques consist of 250 shots shot by an attacker from random distances and angles. The evaluations indicate that both goalkeepers have an improvement in performance over the basic goalkeeper, used by the other teams. The goalkeeper positioning on a line has the best performance, with an increase in performance of 27\% over the basic goalkeeper. These results demonstrate the value of positioning techniques for a goalkeeper in the AI World Cup.