# CRaPI Framework #
by Team Yaffa

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Wednesday, May 28, 2003

Simulated RoboCup - Creating a generic API

The field of simulated RoboCup is still in its infancy, the lack of a stable, well-documented and public API is apparent. This report describes the effort to provide such an API to the RoboCup community. The ambition have been to keep the API agent architecture independent to allow for all possible types of decision makers to be built on top of it.

Due to the inherent noise in the domain, we set out designing the positioning functionality with the use of fuzzy logic. The complexity of this approach, and its inability to keep reasonable execution times when scaling up from the field size of the legged league to the field size of the simulated league, forced us to discard this approach in favour of the simplified positioner now present in the API.

To enable users to immediately start using the API, we have created an example player called Tiro. This player uses the common behaviour model, and can easily be extended to fit a particular purpose.

Wednesday, June 25, 2003

Machine learning in simulated RoboCup

An implementation of the Electric Field Approach applied to the simulated RoboCup is presented, together with a demonstration of a learning system. Results are presented from the optimization of the Electric Field parameters in a limited situation, using the learning system. Learning techniques used in contemporary RoboCup research are also described including a brief presentation of their results.

Approved: 2003-06-10
BibTex entry

Monday, June 30, 2003

Situation assessment and role selection in the simulated RoboCup domain

In the recent world championships of the simulated RoboCup league the winning teams possessed low level behaviours, such as kick and pass, that were close to perfection. In order to improve a team’s performance you will need, beside perfect low level behaviours, a good management of the team.

We present a model for managing a team in the simulated RoboCup league.
The model is based on techniques used by the recent winners in the league and allows you to get a well coordinated team of agents striving for a common goal.

The model supports different formations in different situations, which contributes to a dynamic team play, where the players can adjust to their opponents and other factors like time left and goal difference. For example if the game is near the end and the team is loosing a more risky and aggressive tactic is chosen.

Approved: 2003-06-10
BibTex entry