Fuzzy logic

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Fuzzy logic

  1. 1. Fuzzy Logic Mark Strohmaier CSE 335/435
  2. 2. Outline● What is Fuzzy Logic?● Some general applications● How does Fuzzy Logic apply to IDSS● Real life examples
  3. 3. What is Fuzzy LogicFuzzy Logic was developed by Lotfi Zadeh at UCBerkley“Fuzzy logic is derived from fuzzy set theorydealing with reasoning which is approximate ratherthan precisely deduced from classicalpredicate logic”
  4. 4. Fuzzy Set TheoryIn traditional set theory, an element either belongs toa set, or it does not.Membership functions classify elements in the range[0,1], with 0 and 1 being no and full inclusion, theother values being partial membership
  5. 5. Where did Fuzzy Logic come fromPeople generally do not divide things into cleancategories, yet still make solid, adaptive decisionsDr. Zadeh felt that having controllers to accept noisydata might make them easier to create, and moreeffective
  6. 6. Simple example of Fuzzy LogicControlling a fan:Conventional model – if temperature > X, run fan else, stop fanFuzzy System - if temperature = hot, run fan at full speed if temperature = warm, run fan at moderate speed if temperature = comfortable, maintain fan speed if temperature = cool, slow fan if temperature = cold, stop fanhttp://www.duke.edu/vertices/update/win94/fuzlogic.html
  7. 7. Some Fuzzy Logic applicationsMASSIVECreated to help create the large-scale battle scenesin the Lord of the Rings films, MASSIVE isprogram for generating generating crowd-relatedvisual effects
  8. 8. Applications of Fuzzy LogicVehicle ControlA number of subway systems,particularly in Japan and Europe,are using fuzzy systems tocontrol braking and speed. Oneexample is the Tokyo Monorail
  9. 9. Applications of Fuzzy LogicAppliance control systemsFuzzy logic is starting to be used to help controlappliances ranging from rice cookers to small-scalemicrochips (such as the Freescale 68HC12)
  10. 10. How does fuzzy logic relate to IDSS“One of the most useful aspects of fuzzy set theory is itsability to represent mathematically a class of decisionproblems called multiple objective decisions (MODs).This class of problems often involves many vague andambiguous (and thus fuzzy) goals and constraints.”MODs show up in a number of different IDSS areas – E-commerce, tutoring systems, some recommendersystems, and morehttp://www.fuzzysys.com/fdmtheor.pdf
  11. 11. “A fuzzy decision maker”It can be difficult to distinguish between variousgoals and categories at times *Is a goal in an e-commerce decision hard orsoft? *When is a restaurant crowded, or only slightlycrowded?
  12. 12. One specific Fuzzy logic IDSSThere have been many projects in which fuzzy logichas been combined with IDSS.One common case is in navigational and sensorsystems for roboticsA specific example is:Fuzzy Logic in Autonomous Robot Navigation - a case studyAlessandro SaffiottiCenter for Applied Autonomous Sensor SystemsDept. of Technology, University of Örebro, Sweden
  13. 13. Autonomous RoboticsAutonomous robotic systems are ones which aredesigned to “move purposefully and without humanintervention in environments which have not beenspecifically engineered for it”Example of autonomous systems:the Mars rovers Spirit and Opportunity(the rovers use fuzzy logic in part to help with navigation, sample identification andlearning)
  14. 14. IDSS and Autonomous RoboticsAutonomous Robot Systems require multiplecomponents:1) Pursue goals2) Real Time Reaction3) Build, Use and maintain an environment map4) Plan formulation5) Adaptation to the environment
  15. 15. Autonomous Robot Architecture
  16. 16. Parts using Fuzzy LogicFuzzy techniques have been used to1) implement basic behaviors which tolerateuncertainty2) coordinate multiple actions to reach a goal3) help the robot remember where it is with respect toits map
  17. 17. Basic Behaviors using Fuzzy LogicEach behavior is described in terms of a desirabilityfunction, based on the current state and the variouscontrols active:
  18. 18. Basic Behaviors using Fuzzy Logic (Out of reach means it is too close to pick up)
  19. 19. Behavior Coordination
  20. 20. Using Map Information
  21. 21. ConclusionsFuzzy Logic is a different, but still effective, type of logic and knowledge representationCan be applied to numerous areas, especially robotics It can also be applied effectively to IDSS and decision making

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