Artificial Intelligence

I started working with AI in 2010, when I enrolled in the PhD program at IME-USP. Professor Junior Barrera has been guiding me from the start.

The motivation that originated this work was the desire to create a team invasion sports simulator capable of applying user defined strategies to guide the behavior of the agents in the simulation. With this objective in mind we created a formal strategy model to describe complex team behavior and developed methods of using that model to calculate collective plans. We defined both the strategy model and the planning methods in a broad manner that can be applied in many different domains. Then we defined a basketball simulation domain and implemented our methodology to develop a simulator. We also present a control system architecture that is compatible with our proposed planner and show how we implemented it to create the basketball simulator.

The formal strategy model we developed can be used to represent team behavior, analyze real world events and create simulations. We developed a strategy design tool that allows the end user to create and visualize team strategies for basketball. Finally, we developed a system that interprets the user generated strategies and creates a basketball match simulation of the described behavior.

We also proposed a methodology for the development of simulation systems involving multiple intelligent agents. Our recommended control system architecture separates the many layers of control, which simplifies the development process and results in a naturally expansible system.

The result so far can be summarized by two images, the first one shows the input for the simulator, which is generated by the user, and the second shows the simulation result.

Frame 1: the red team’s offensive strategy specification. Frame 2: the blue team’s defensive strategy specification. Frame 3: testing the play highlighted in red in frame 1. Frame 4: choosing the simulation preferences.

The execution of the basketball simulator with the input from the previous image. Column A shows 4 steps of the simulation of a play. Column B shows the corresponding point in the offensive strategy of the red team. Column C shows the corresponding point in the defensive TSM of player 2 in the blue team, marking red player 2.

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