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Fuzzy Logic




              Mark Strohmaier

               CSE 335/435
Outline

●
    What is Fuzzy Logic?

●
    Some general applications

●
    How does Fuzzy Logic apply to IDSS

●
    Real life examples
What is Fuzzy Logic

Fuzzy Logic was developed by Lotfi Zadeh at UC
Berkley

“Fuzzy logic is derived from fuzzy set theory
dealing with reasoning which is approximate rather
than precisely deduced from classical
predicate logic”
Fuzzy Set Theory



In 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 membership
Where did Fuzzy Logic come from


People generally do not divide things into clean
categories, yet still make solid, adaptive decisions


Dr. Zadeh felt that having controllers to accept 'noisy'
data might make them easier to create, and more
effective
Simple example of Fuzzy Logic
Controlling a fan:

Conventional model –
      if temperature > X, run fan
      else, stop fan

Fuzzy 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 fan

http://www.duke.edu/vertices/update/win94/fuzlogic.html
Some Fuzzy Logic applications
MASSIVE

Created to help create the large-scale battle scenes
in the Lord of the Rings films, MASSIVE is
program for generating generating crowd-related
visual effects
Applications of Fuzzy Logic

Vehicle Control

A number of subway systems,
particularly in Japan and Europe,
are using fuzzy systems to
control braking and speed. One
example is the Tokyo Monorail
Applications of Fuzzy Logic



Appliance control systems


Fuzzy 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 more

http://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 IDSS

There have been many projects in which fuzzy logic
has been combined with IDSS.

One common case is in navigational and sensor
systems for robotics

A specific example is:
Fuzzy Logic in Autonomous Robot Navigation - a case study
Alessandro Saffiotti
Center for Applied Autonomous Sensor Systems
Dept. of Technology, University of Örebro, Sweden
Autonomous Robotics


Autonomous 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 Robotics

Autonomous Robot Systems require multiple
components:

1) Pursue goals
2) Real Time Reaction
3) Build, Use and maintain an environment map
4) Plan formulation
5) Adaptation to the environment
Autonomous Robot Architecture
Parts using Fuzzy Logic


Fuzzy techniques have been used to

1) implement basic behaviors which tolerate
uncertainty

2) coordinate multiple actions to reach a goal

3) help the robot remember where it is with respect to
its map
Basic Behaviors using Fuzzy Logic




Each 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 Coordination
Using Map Information
Conclusions



Fuzzy Logic is a different, but still effective, type of
       logic and knowledge representation

Can be applied to numerous areas, especially robotics

    It can also be applied effectively to IDSS and
                    decision making

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Fuzzy logic

  • 1. Fuzzy Logic Mark Strohmaier CSE 335/435
  • 2. Outline ● What is Fuzzy Logic? ● Some general applications ● How does Fuzzy Logic apply to IDSS ● Real life examples
  • 3. What is Fuzzy Logic Fuzzy Logic was developed by Lotfi Zadeh at UC Berkley “Fuzzy logic is derived from fuzzy set theory dealing with reasoning which is approximate rather than precisely deduced from classical predicate logic”
  • 4. Fuzzy Set Theory In 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 membership
  • 5. Where did Fuzzy Logic come from People generally do not divide things into clean categories, yet still make solid, adaptive decisions Dr. Zadeh felt that having controllers to accept 'noisy' data might make them easier to create, and more effective
  • 6. Simple example of Fuzzy Logic Controlling a fan: Conventional model – if temperature > X, run fan else, stop fan Fuzzy 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 fan http://www.duke.edu/vertices/update/win94/fuzlogic.html
  • 7. Some Fuzzy Logic applications MASSIVE Created to help create the large-scale battle scenes in the Lord of the Rings films, MASSIVE is program for generating generating crowd-related visual effects
  • 8. Applications of Fuzzy Logic Vehicle Control A number of subway systems, particularly in Japan and Europe, are using fuzzy systems to control braking and speed. One example is the Tokyo Monorail
  • 9. Applications of Fuzzy Logic Appliance control systems Fuzzy logic is starting to be used to help control appliances ranging from rice cookers to small-scale microchips (such as the Freescale 68HC12)
  • 10. 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 more http://www.fuzzysys.com/fdmtheor.pdf
  • 11. “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?
  • 12. One specific Fuzzy logic IDSS There have been many projects in which fuzzy logic has been combined with IDSS. One common case is in navigational and sensor systems for robotics A specific example is: Fuzzy Logic in Autonomous Robot Navigation - a case study Alessandro Saffiotti Center for Applied Autonomous Sensor Systems Dept. of Technology, University of Örebro, Sweden
  • 13. Autonomous Robotics Autonomous 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)
  • 14. IDSS and Autonomous Robotics Autonomous Robot Systems require multiple components: 1) Pursue goals 2) Real Time Reaction 3) Build, Use and maintain an environment map 4) Plan formulation 5) Adaptation to the environment
  • 16. Parts using Fuzzy Logic Fuzzy techniques have been used to 1) implement basic behaviors which tolerate uncertainty 2) coordinate multiple actions to reach a goal 3) help the robot remember where it is with respect to its map
  • 17. Basic Behaviors using Fuzzy Logic Each behavior is described in terms of a desirability function, based on the current state and the various controls active:
  • 18. Basic Behaviors using Fuzzy Logic (Out of reach means it is too close to pick up)
  • 21. Conclusions Fuzzy Logic is a different, but still effective, type of logic and knowledge representation Can be applied to numerous areas, especially robotics It can also be applied effectively to IDSS and decision making