Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Fuzzy LogicMark StrohmaierCSE 335/435Outline●What is Fuzzy Logic?●Some general applications●How does Fuzzy Logic apply to IDSS●Real life examplesWhat is Fuzzy LogicFuzzy Logic was developed by Lotfi Zadeh at UC Berkley“Fuzzy logic is derived from fuzzy set theorydealing with reasoning which is approximate rather than precisely deduced from classical predicate logic”Fuzzy Set TheoryIn traditional set theory, an element either belongs to a set, or it does not. Membership functions classify elements in the range [0,1], with 0 and 1 being no and full inclusion, the other values being partial membershipWhere did Fuzzy Logic come fromPeople generally do not divide things into clean categories, yet still make solid, adaptive decisionsDr. Zadeh felt that having controllers to accept 'noisy' data might make them easier to create, and more effectiveSimple example of Fuzzy LogicControlling a fan:Conventional model – if temperature > X, run fanelse, stop fanFuzzy System - if temperature = hot, run fan at full speedif temperature = warm, run fan at moderate speedif temperature = comfortable, maintain fan speedif temperature = cool, slow fanif temperature = cold, stop fanhttp://www.duke.edu/vertices/update/win94/fuzlogic.htmlSome Fuzzy Logic applicationsMASSIVECreated to help create the large-scale battle scenes in the Lord of the Rings films, MASSIVE is program for generating generating crowd-related visual effectsApplications of Fuzzy LogicVehicle ControlA number of subway systems, particularly in Japan and Europe, are using fuzzy systems to control braking and speed. One example is the Tokyo MonorailApplications of Fuzzy LogicAppliance control systemsFuzzy logic is starting to be used to help control appliances ranging from rice cookers to small-scale microchips (such as the Freescale 68HC12)How does fuzzy logic relate to IDSS“One of the most useful aspects of fuzzy set theory is its ability to represent mathematically a class of decision problems called multiple objective decisions (MODs). This class of problems often involves many vague and ambiguous (and thus fuzzy) goals and constraints.”MODs show up in a number of different IDSS areas – E-commerce, tutoring systems, some recommender systems, and morehttp://www.fuzzysys.com/fdmtheor.pdf“A fuzzy decision maker”It can be difficult to distinguish between various goals and categories at times*Is a goal in an e-commerce decision hard or soft?*When is a restaurant crowded, or only slightly crowded?One specific Fuzzy logic IDSSThere have been many projects in which fuzzy logic has been combined with IDSS.One common case is in navigational and sensor systems 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, SwedenAutonomous RoboticsAutonomous robotic systems are ones which are designed to “move purposefully and without human intervention in environments which have not been specifically 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 and learning)IDSS and Autonomous RoboticsAutonomous Robot Systems require multiple components:1) Pursue goals2) Real Time Reaction3) Build, Use and maintain an environment map4) Plan formulation5) Adaptation to the environmentAutonomous Robot ArchitectureParts using Fuzzy LogicFuzzy techniques have been used to 1) implement basic behaviors which tolerate uncertainty2) coordinate multiple actions to reach a goal3) help the robot remember where it is with respect to its mapBasic Behaviors using Fuzzy LogicEach behavior is described in terms of a desirability function, based on the current state and the various controls active:Basic Behaviors using Fuzzy Logic(Out of reach means it is too close to pick up)Behavior CoordinationUsing Map InformationConclusionsFuzzy Logic is a different, but still effective, type of logic and knowledge representationCan be applied to numerous areas, especially roboticsIt can also be applied effectively to IDSS and decision