Fuzzy Disjunctive Inference from the Perspective of a Dweeb Robert J. Marks II.

Slides:



Advertisements
Similar presentations
Graphical Technique of Inference
Advertisements

Naïve-Bayes Classifiers Business Intelligence for Managers.
Fuzzy Expert System  An expert might say, “ Though the power transformer is slightly overloaded, I can keep this load for a while”.  Another expert.
1 Neural networks. Neural networks are made up of many artificial neurons. Each input into the neuron has its own weight associated with it illustrated.
AI – CS364 Fuzzy Logic Fuzzy Logic 3 03 rd October 2006 Dr Bogdan L. Vrusias
Copyright 2004 Koren & Krishna ECE655/DataRepl.1 Fall 2006 UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering Fault Tolerant Computing.
Neural Network I Week 7 1. Team Homework Assignment #9 Read pp. 327 – 334 and the Week 7 slide. Design a neural network for XOR (Exclusive OR) Explore.
Particle Swarm Optimization (PSO)  Kennedy, J., Eberhart, R. C. (1995). Particle swarm optimization. Proc. IEEE International Conference.
POSTER TEMPLATE BY: Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification Prasanna Tamilselvan.
Foundations of Comparative Analytics for Uncertainty in Graphs Lise Getoor, University of Maryland Alex Pang, UC Santa Cruz Lisa Singh, Georgetown University.
Publication Venues Main Neural Network Conferences –NIPS (Neural Information Processing Systems) –IJCNN (Intl Joint Conf on Neural Networks) Main Neural.
CONTENT BASED FACE RECOGNITION Ankur Jain 01D05007 Pranshu Sharma Prashant Baronia 01D05005 Swapnil Zarekar 01D05001 Under the guidance of Prof.
Information Agents for Autonomous Acquisition of Sensor Network Data A. Rogers and N. R. Jennings University of Southampton, UK M. A. Osborne and S. J.
AI – CS364 Hybrid Intelligent Systems Overview of Hybrid Intelligent Systems 07 th November 2005 Dr Bogdan L. Vrusias
CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Dr. Daniel Tauritz.
Introduction to Fuzzy Logic Control
Face Recognition Using EigenFaces Presentation by: Zia Ahmed Shaikh (P/IT/2K15/07) Authors: Matthew A. Turk and Alex P. Pentland Vision and Modeling Group,
Particle Swarm Optimization Algorithms
CPSC 386 Artificial Intelligence Ellen Walker Hiram College
CHAPTER 12 ADVANCED INTELLIGENT SYSTEMS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang.
Presented By Ali Rıza KONAN Bogazici University
KOHONEN SELF ORGANISING MAP SEMINAR BY M.V.MAHENDRAN., Reg no: III SEM, M.E., Control And Instrumentation Engg.
Department of Information Technology Indian Institute of Information Technology and Management Gwalior AASF hIQ 1 st Nov ‘09 Department of Information.
1 Swarm Intelligence: The Method Behind the Mobs Robert J. Marks II Distinguished Professor of Electrical & Computer Engineering, Baylor University Bio-Engineering.
POWER CONTROL IN COGNITIVE RADIO SYSTEMS BASED ON SPECTRUM SENSING SIDE INFORMATION Karama Hamdi, Wei Zhang, and Khaled Ben Letaief The Hong Kong University.
Optimal Power Control, Rate Adaptation and Scheduling for UWB-Based Wireless Networked Control Systems Sinem Coleri Ergen (joint with Yalcin Sadi) Wireless.
Artificial Intelligence in Game Design Lecture 6: Fuzzy Logic and Fuzzy State Machines.
Robert Jackson Marks II2 Applications: Warfare & Game Theory Aviation Weekly, Sept 29, 2003.
1 Swarm Intelligence: The Method Behind the Mobs Robert J. Marks II Distinguished Professor of Electrical & Computer Engineering, Baylor University Bio-Engineering.
Fuzzy Inference (Expert) System
Mobile Robot Navigation Using Fuzzy logic Controller
Detection, Classification and Tracking in a Distributed Wireless Sensor Network Presenter: Hui Cao.
Artificial Intelligence Chapter 3 Neural Networks Artificial Intelligence Chapter 3 Neural Networks Biointelligence Lab School of Computer Sci. & Eng.
Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective.
Soft Computing Lecture 19 Part 2 Hybrid Intelligent Systems.
Fuzzy Systems Michael J. Watts
PART 9 Fuzzy Systems 1. Fuzzy controllers 2. Fuzzy systems and NNs 3. Fuzzy neural networks 4. Fuzzy Automata 5. Fuzzy dynamic systems FUZZY SETS AND FUZZY.
Subsumption Architecture and Nouvelle AI Arpit Maheshwari Nihit Gupta Saransh Gupta Swapnil Srivastava.
The ZebraNet Wild Life Tracker Department of Electrical Engineering Princeton University.
Neural Network Application for Fault Analysis
Simulating Crowds Simulating Dynamical Features of Escape Panic & Self-Organization Phenomena in Pedestrian Crowds Papers by Helbing.
CS Machine Learning Instance Based Learning (Adapted from various sources)
A field of study that encompasses computational techniques for performing tasks that require intelligence when performed by humans. Simulation of human.
1 Lecture 4 The Fuzzy Controller design. 2 By a fuzzy logic controller (FLC) we mean a control law that is described by a knowledge-based system consisting.
Particle Filter for Robot Localization Vuk Malbasa.
IEEE AI - BASED POWER SYSTEM TRANSIENT SECURITY ASSESSMENT Dr. Hossam Talaat Dept. of Electrical Power & Machines Faculty of Engineering - Ain Shams.
Computational Intelligence: Methods and Applications Lecture 26 Density estimation, Expectation Maximization. Włodzisław Duch Dept. of Informatics, UMK.
Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features Qing Chen Discover Lab, SITE, University of Ottawa May 2, 2006.
A PRELIMINARY EMPIRICAL ASSESSMENT OF SIMILARITY FOR COMBINATORIAL INTERACTION TESTING OF SOFTWARE PRODUCT LINES Stefan Fischer Roberto E. Lopez-Herrejon.
Evolving robot brains using vision Lisa Meeden Computer Science Department Swarthmore College.
A Presentation on Adaptive Neuro-Fuzzy Inference System using Particle Swarm Optimization and it’s Application By Sumanta Kundu (En.R.No.
Mohsen Riahi Manesh and Dr. Naima Kaabouch
COGNITIVE APPROACH TO ROBOT SPATIAL MAPPING
FUZZY NEURAL NETWORKS TECHNIQUES AND THEIR APPLICATIONS
Creating fuzzy rules from numerical data using a neural network
Introduction to Fuzzy Logic
Fuzzy logic Introduction 3 Fuzzy Inference Aleksandar Rakić
Dr. Unnikrishnan P.C. Professor, EEE
Dr. Unnikrishnan P.C. Professor, EEE
Dr. Unnikrishnan P.C. Professor, EEE
Instance Based Learning (Adapted from various sources)
A thesis Presented by: Firasath Riyaz. Mentor: Dr. Peter M. Maurer.
Artificial Intelligence Chapter 3 Neural Networks
Artificial Intelligence Chapter 3 Neural Networks
Robot Intelligence Kevin Warwick.
Dr. Unnikrishnan P.C. Professor, EEE
Artificial Intelligence Chapter 3 Neural Networks
Artificial Intelligence Chapter 3 Neural Networks
Network Protocols Exploration.
Artificial Intelligence Chapter 3 Neural Networks
Presentation transcript:

Fuzzy Disjunctive Inference from the Perspective of a Dweeb Robert J. Marks II

CONJUNCTIVE Approach Do this 1 and this 2 and this 3 and this 4 and this 5 to get that. Result: Highly complex and brittle design. Loose this 4 and your system can fail. Conjunctive statement:

DISJUNCTIVE Approach (Do this 1 to get that ) or (Do this 2 to get that ) or (Do this 3 to get that ) or (Do this 4 to get that ) Result: Highly robust and fault tolerant design. Loose this 4 and you’re still in business. Disjunctive statement:

Is… DISJUNCTIVE = CONJUNCTIVE? Is… (Do this 1 to get that ) or (Do this 2 to get that ) or (Do this 3 to get that ) or (Do this 4 to get that ) = (Do this 1 and this 2 and this 3 and this 4 ) to get that. ??? In a Boolean sense,

Disjunctive vs. Conjunctive Disjunctive reasoning sometimes referred to as “The Combs Method”* Examples of Complex Disjunctive Systems Examples of Complex Disjunctive Systems 1.Swarms: Insects & People 2.Your Body 3.Animal motor functions 4.Genomic symbiogenesis William E. Combs * Earl Cox, The Fuzzy Systems Handbook, Academic Press/ Morgan Kaufman. J. J. Weinschenk, W. E. Combs, R. J. Marks II, “Avoidance of rule explosion by mapping fuzzy systems to a disjunctive rule configuration,” IEEE Int’l Conference on Fuzzy Systems, St. Louis, MO, 2003, pp J. J. Weinschenk, R. J. Marks II, W. E. Combs, “Layered URC fuzzy systems: a novel link between fuzzy systems and neural networks,” Proc. IEEE Intl’ Joint Conf. on Neural Networks, Portland, OR, 2003, pp Jeffrey J. Weinschenk, William E. Combs, Robert J. Marks II, "On the avoidance of rule explosion in fuzzy inference engines, " International Journal of Information Technology and Intelligent Computing, vol.1, #4 (2007).

DR vs. CR Scorecard PropertyConjunctive Reasoning (CR)Disjunctive Reasoning (DR) ScalabilityExponentialLinear PlasticityRigidPlastic CouplingHighLow RobustnessLowHigh Fault Tolerance LowHigh Cognitive Parallel For low order systems, CR most closely parallels human cognitive inference.. For complex systems, DR most closely parallels human cognitive inference. Parallel & Distributed Processing Ability Parallel and distributed processing increases the complexity of most properties. DR is readily applied to distributed processing as each unit has a relationship with the consequent that is independent of the other units.

Bullies and Dweebs Physics of Dweebs & Bullies Fixed Playground Momentum Bounce off of walls Maximum Speed Bullies Fixed Speed Fixed twiddle Follows closest dweeb

Bullies and Dweebs Dweeb Variables Avoid Walls Avoid Bullies Adjustable Twiddle Avoid infected dweebs (?) Other?

A Disjunctive Rule... IF the Dweeb is VERY CLOSE to the right wall, THEN increase the speed to the left A LOT. IF the Dweeb is CLOSE to the right wall, THEN increase the speed to the left SOME. IF the Dweeb is NOT CLOSE to the right wall, THEN leave the speed AS IS.

A Disjunctive Rule... 0 L Not Close Close Very Close LL ML Z MR LR - Delta V x MAX 0 Delta Vx MAX Distance to Right Wall Aggregate at fuzzy level? Or after defuzzification?

A Disjunctive Rule... 0 L Not Close Close Very Close LL ML Z MR LR - Delta V x MAX 0 Delta Vx MAX Distance to Right Wall After defuzzification

A Disjunctive Rule... 0 L Not Close Close Very Close LL ML Z MR LR - Delta V x MAX 0 Delta Vx MAX Distance to Right Wall

A Disjunctive Rule...Same As L -Delta Vx MAX -Delta Vx Distance to the Right Wall

Another Disjunctive Rule... x Distance to closest Bully LL ML Z MR LR - Delta V x MAX 0 Delta Vx MAX NL NM Z PM P L SAME CONSEQUENT!

A Disjunctive Rule...Same As 0 -Delta Vx MAX -Delta Vx Distance to Nearest Bully

Disjunctively Combine 0 How do aggregate these two consequents? Weighted Average? Most urgent?

A Disjunctive Rule... - Delta V x MAX 0 Delta Vx MAX Before defuzzification Distance to Right Wall LL ML Z MR LR Dweeb Distance Disjunctive Aggregatation Followed by Defuzzification

Bullies and Dweebs Dweeb Variables Avoid Walls (Velocity x & y) Avoid Bullies (Velocity x & y) Avoid infected dweebs (Velocity x & y) Avoid infected dweebs (?) Other?

Assume... The Dweebs will have sensors allowing them to detect: – The closest bully – The distance to all walls – The distance to all four corners – The closest infected Dweeb – Other ??

Assignment Write a Bullies & Dweeb simulation. The Bullies will have twiddle and maximum speed. They pursue dweebs. They are fixed. Choose disjunctive mappings so that the dweebs survive well. Sample software for similar simulation is at NeoSwarm.com We will later evolve the swarm.