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ML and KR Techniques for Knowledge Representation

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1. The document discusses different approaches to knowledge representation and machine learning including first order logic, artificial neural networks, Bayesian networks, and reinforcement learning. 2. Artificial neural networks can represent complex functions by learning through backpropagation but lack interpretability, while Bayesian networks combine logic and learning from experience under uncertainty. 3. Reinforcement learning defines rewards and punishments to allow agents to discover optimal policies without being explicitly programmed through interactions with an environment.

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Knowledge Representation and Machine Learning Stephen J. Guy
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Representation? ,[object Object],[object Object],[object Object],[object Object]
Knowledge Representation? ,[object Object],[object Object]
Early Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Early Work - Theme ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Generality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
First order Logic - Problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
First order Logic - Bigger Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Artificial Neural Networks (ANN) ,[object Object],[object Object],[object Object],Inputs: I 1 , I 2 , … Output: O Responses: R 1 , R 2 , …
Artificial Neural Networks (ANN) ,[object Object],[object Object],[object Object],[object Object],Inputs: I 1 , I 2 , … Output: O Responses: R 1 , R 2 , …
Single Layer feed-forward ANNs (Perceptrons) Input Layer Output Unit ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning in Perceptrons ,[object Object],[object Object],[object Object],[object Object],[object Object]
Multi Layer feed-forward ANNs ,[object Object],[object Object],Input Layer Output Unit Hidden Layer
Learning in Multilayer ANNs (1/2) ,[object Object],[object Object],[object Object]
Learning in Multilayer ANNs (2/2) ,[object Object],[object Object],[object Object]
ANN - Summery ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ANN – In Robots (Simple) ,[object Object],[object Object],[object Object],[object Object]
ANN – In Robots (Complex) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Networks ,[object Object],[object Object],[object Object]
Bayes’ Rule ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],P(M) = 1/50000 M P(S) T .5 F 1/20 Meningitis Stiff Neck
Bayesian Networks (Temporal Models) ,[object Object],[object Object],[object Object],Rain t-1 Umbrella t-1 Rain t Umbrella t Rain t+1 Umbrella t+1
Bayesian Networks (Temporal Models) ,[object Object],[object Object],[object Object],[object Object],[object Object],Rain t-1 Umbrella t-1 Rain t Umbrella t Rain t+1 Umbrella t+1
Bayesian Networks (Temporal Models) ,[object Object],[object Object],[object Object],[object Object],[object Object],Rain t-1 Umbrella t-1 Rain t Umbrella t Rain t+1 Umbrella t+1
Bayesian Networks (Temporal Models) ,[object Object],[object Object],[object Object],[object Object],[object Object],Rain t-1 Umbrella t-1 Rain t Umbrella t Rain t+1 Umbrella t+1
Bayesian Networks - Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Networks in Robotics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Robot going through doorway using Bayesian networks (Univ. of Basque)
Reinforcement Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reinforcement Learning - Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],start -1 1 .1 .1 .8
Reinforcement Learning - Policy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],start -1 1
Reinforcement Learning - Utility ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],1 2 3 1 2 3 4 S’ 1 2 3 1 2 3 4
Reinforcement Learning – Policy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reinforcement Learning in Robotics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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ML and KR Techniques for Knowledge Representation

  • 1. Knowledge Representation and Machine Learning Stephen J. Guy
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Editor's Notes

  1. http://gizmodo.com/gadgets/robots/kurzweil-foresees-borgs-by-2045-128119.php
  2. Quote from R&N (pg 19) STUDENT developed by Daniel Bobrow Image from (http://library.thinkquest.org/2705/Programs.html)
  3. “ Can capture commonsense …” R&N pg 240
  4. PROLOG programming language allows programming entirely in first order logic
  5. All we knew was “brains are binary” “have neuraons”
  6. Proof by (McCulloch & Pitts, 1943)
  7. Minkeys result killed ANNs for over a decade
  8. For threshold function g’(in) term is ommited
  9. Optimal Brain Damage Optimal
  10. http://www.generation5.org/content/2005/neuroLego.asp
  11. Nrm, mean normalize the vector, example Nrm<.3,.4> = <.6,.8>
  12. http://citeseer.ist.psu.edu/cache/papers/cs/32945/http:zSzzSzscsx01.sc.ehu.eszSzccwrobotzSzframeszSzpublicationszSzpaperszSzlazkano03door.pdf/door-crossing-behavior-for.pdf http://www.actapress.com/PaperInfo.aspx?PaperID=23199
  13. Example from R&N
  14. ADP and TD can both be modified to handle Active Reinforment
  15. U(1,4) = 1, U(2,4) = -1 S’ is neighboring states to s
  16. http://www.tecnun.es/asignaturas/control1/proyectos/pdobleinv/evideo.htm