1. Results Objectives Overall Objective: Use type-2 fuzzy logic to create a team of robots that learn from their environment to work effectively and collaborate within an uncertain spatio- temporal environment. 1.Develop Type-2 fuzzy logic software for the MATLAB environment using the given benchmark problem as a guide and verifier 2.Implement type-2 fuzzy logic in the collaborative robotic Pong environment (FLIP) 3.Create a robotic coach that uses type-2 fuzzy logic to alter the logic of the players, allowing them to learn from and beat their opponent. (CLIFF) Methods – Type-2 Fuzzy Software Three Major Steps: 1.Decide on what kind of type-2 logic to use 2.Create MATLAB code 3.Verify with benchmark problem Step 1: Decided to use a Gaussian singleton interval type-2 fuzzy inference system (Gauss-INST2-FIS) Primary membership function is a Gaussian function Constant mean (m) Variable standard deviation (σ, σ 1, σ 2 ) Step 2: Created several MATLAB functions Specific to the type-2 architecture being used Shape function Function to communicate between the dynamic system and the type-2 system Different than what is used for type-1 logic Step 3: Verify the created type-2 fuzzy logic system by creating and improving on the results presented in the benchmark problem [2] Determine the dynamic equations for filling the second tank with water through controlling the pump that pours water into tank 1 Re-create the same input type-1 and type-2 membership functions to make their results Tweak to understand the system and improve upon their results Why? Better understanding of type-2 fuzzy logic and its benefits Ensure that the created type-2 fuzzy logic MATLAB system works by having it produce verifiable results. Conclusion It is expected that the type-2 fuzzy logic system that has been developed will be able to re-create the benchmark problem, as well as improve the playing ability of the FLIP team. Their ability will be greatly improved through the use of a robotic coach who will change their logic during the game, simulating learning from its opponent. Future work will include finishing the robotic coach system, and pursuit of further applications with this newly created type-2 learning system. Publication of this research is anticipated. References [1] Baklouti, Nesrine, Robert John, and Adel Alimi. "Interval Type-2 Fuzzy Logic Control of Mobile Robots."Journal of Intelligent Learning Systems and Applications. 4.November 2012 (2012): 291-302. Web. 18 Feb. 2013. [2] Dongrui Wu, Woei Wan Tan, Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, Engineering Applications of Artificial Intelligence, Volume 19, Issue 8, December 2006, Pages 829-841, ISSN 0952- 1976, 10.1016/j.engappai.2005.12.011. (http://www.sciencedirect.com/science/article/pii/S0952197606000388) [3] Mendel, Jerry. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice Hall PTR, 2001. Print. [4] Castillo, Oscar, and Patricia Melin. Type-2 Fuzzy Logic: Theory and Applications. 1. Heidelberg: Springer, 2008. Print. [5] Castillo, Oscar. Type-2 Fuzzy Logic in Intelligent Control Applications. 1. Heidelberg: Springer, 2012. eBook. Figure 4: Example and parts of a Gaussian type-2 membership function Collaborative Learning using Fuzzy Inferencing: Part I Sophia Mitchell and Dr. Kelly Cohen University of Cincinnati, School of Aerospace Systems Figure 5: Schematic Diagram for the coupled tank filling benchmark problem [2] Introduction There are a growing number of aerospace applications demonstrating the effectiveness of emulating human decision making using fuzzy logic. The main research challenges include situational awareness and decision making in an uncertain time critical spatio-temporal environment. A completely autonomous, collaborative team of robots that can learn from their surroundings and act accordingly would be very useful in applications including, but not limited to, space robotics, celestial body exploration & colonization, disaster relief, Unmanned Aerial Vehicles, and homeland security. Type-1 Fuzzy Logic Allows for classification of variables for more humanlike reasoning Bypasses the traditional binary logic of computer science Control along a continuum Reasoning is linguistic Type-2 Fuzzy Logic Allows for more noisy measurements to be quantified More complex linguistic reasoning – closer to humanlike thinking Brings uncertainty into the membership function itself BaldNot Bald Percent of hair on head 0 25 50 75 100 Equations 2 and 3: Constants and derived dynamic equation for the tank filling benchmark problem [2] Equation 1: Gaussian Function Figure 3: A type-2 fuzzy logic system Figure 1: A type-1 fuzzy logic system Figure 2: A simple example of fuzzification Figure 6: Plot of the type-2 input membership functions for input 1 Figure 7: Plot of the type-2 input membership functions for input 2 Figure 8: Results produced by the benchmark paper compared to results produced by the created algorithm.