Collaborative Learning using Fuzzy Inferencing (CLIFF) Part 1

There are a growing number of aerospace applications demonstrating the effectiveness of emulating human decision making using fuzzy logic. In this effort, a previously created MATLAB simulation of the classic arcade game PONG is utilized in which a fuzzy logic system that uses real-time fuzzy reasoning and awareness to represent a two-player team is able to play against various opponents. The overall project has three main parts: creation and verification of a type-2 fuzzy logic system in MATLAB, implementation of this new type of logic through conversion of the fuzzy robotic team to type2 fuzzy logic, and creating a fuzzy robotic coach who has the ability to better the team based upon its ability to learn using type-2 fuzzy logic. In this effort, part one was attempted, and, through extensive studying and understanding of type-2 fuzzy theory, a type-2 fuzzy logic system was created in the MATLAB environment. This system was then verified by having it re-create and then improve upon the results of a found research paper that controlled the water level in a complex tank system. The verification was considered successful, allowing for development of part two of this research to begin in the future. Overall, this development of intelligence and learning should prove to be an interesting stepping stone towards highly capable robots with the ability to learn from their environment; something that would be extremely helpful in disaster recovery, space exploration and military