Teaching an Agent by Playing a Multimodal Memory Game: Challenges for Machine Learners and Human Teachers AAAI 2009 Spring Symposium: Agents that Learn.

Slides:

Advertisements

Similar presentations

Approaches, Tools, and Applications Islam A. El-Shaarawy Shoubra Faculty of Eng.

Advertisements

Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California

Cognitive Systems, ICANN panel, Q1 What is machine intelligence, as beyond pattern matching, classification and prediction. What is machine intelligence,

Thomas Trappenberg Autonomous Robotics: Supervised and unsupervised learning.

ESP410 Human Movement Pedagogy 3

AI Practice 05 / 07 Sang-Woo Lee. 1.Usage of SVM and Decision Tree in Weka 2.Amplification about Final Project Spec 3.SVM – State of the Art in Classification.

Intelligent Environments1 Computer Science and Engineering University of Texas at Arlington.

Introduction to Machine Learning Algorithms. 2 What is Artificial Intelligence (AI)? Design and study of computer programs that behave intelligently.

Semester wrap-up …my final slides.. More on HCI Class on Ubiquitous Computing next spring Courses in visualization, virtual reality, gaming, etc. where.

Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)

Learning Programs Danielle and Joseph Bennett (and Lorelei) 4 December 2007.

Statistical Learning: Pattern Classification, Prediction, and Control Peter Bartlett August 2002, UC Berkeley CIS.

Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Cognitive Robots © 2014, SNU CSE Biointelligence Lab.,

Metacognitive Issues in Student Engagement Jennifer Berg & Laura Baker.

General Information Course Id: COSC6342 Machine Learning Time: MO/WE 2:30-4p Instructor: Christoph F. Eick Classroom:SEC 201

Revision Michael J. Watts

Hypernetwork Models of Memory Hypernetwork Models of Memory Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering Brain.

Cognitive Learning and the Multimodal Memory Game: Toward Human-Level Machine Learning 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)

Introduction Many decision making problems in real life

Kernel Classifiers from a Machine Learning Perspective (sec ) Jin-San Yang Biointelligence Laboratory School of Computer Science and Engineering.

Artificial Neural Networks An Overview and Analysis.

1 6. Feed-forward mapping networks Lecture Notes on Brain and Computation Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering.

Towards Cognitive Robotics Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Christian.

Chapter 1. Introduction in Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans Jo, HwiYeol Biointelligence Laboratory.

Machine Learning Lecture 1. Course Information Text book “Introduction to Machine Learning” by Ethem Alpaydin, MIT Press. Reference book “Data Mining.

MICHAEL FINE Artificial Intelligence and The Singularity 1.

Self-Assemblying Hypernetworks for Cognitive Learning of Linguistic Memory Int. Conf. on Cognitive Science, CESSE-2008, Feb. 6-8, 2008, Sheraton Hotel,

Using Interactive Evolution for Exploratory Data Analysis Tomáš Řehořek Czech Technical University in Prague.

Incorporating Self Regulated Learning Techniques into Learning by Teaching Environments Biswas, G., Leelawong, K. and Belynne, K. The twenty sixth Annual.

Data Mining and Decision Trees 1.Data Mining and Biological Information 2.Data Mining and Machine Learning Techniques 3.Decision trees and C5 4.Applications.

Pac-Man AI using GA. Why Machine Learning in Video Games? Better player experience Agents can adapt to player Increased variety of agent behaviors Ever-changing.

Course Overview  What is AI?  What are the Major Challenges?  What are the Main Techniques?  Where are we failing, and why?  Step back and look at.

IST_Seminar II CHAPTER 12 Instructional Methods. Objectives: Students will: Explain the role of all teachers in the development of critical thinking skills.

Approaches to Selection and their Effect on Fitness Modelling in an Estimation of Distribution Algorithm A. E. I. Brownlee, J. A. McCall, Q. Zhang and.

Ben Goertzel, PhD Novamente LLC Biomind LLC Artificial General Intelligence Research Institute Virginia Tech, Applied Research Lab for National and Homeland.

Modeling Situated Language Learning in Early Childhood via Hypernetworks Zhang, Byoung-Tak 1,2, Lee, Eun Seok 2, Heo, Min-Oh 1, and Kang, Myounggu 1 1.

Chapter 14. Active Vision for Goal-Oriented Humanoid Robot Walking (2/2) in Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from.

Self-Regulated Learning Students take charge of their own learning.

How does Your Child Learn? Skills Progression and Development.

Chapter 15. Cognitive Adequacy in Brain- Like Intelligence in Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans Cinarel, Ceyda.

DI for the 21 st Century Classroom. Four Principles of Brain- Compatible Learning To set the stage for a differentiated learning environment we first.

5. Methodology Compare the performance of XCS with an implementation of C4.5, a decision tree algorithm called J48, on the reminder generation task. Exemplar.

Beginning Social Communication High School: Lesson Five.

Bayesian Brain Probabilistic Approaches to Neural Coding 1.1 A Probability Primer Bayesian Brain Probabilistic Approaches to Neural Coding 1.1 A Probability.

Brain, Mind, and Computation Part III: Cognitive Brain Networks Brain, Mind, and Computation Part III: Cognitive Brain Networks Brain-Mind-Behavior Seminar.

Learning Analytics isn’t new Ways in which we might build on the long history of adaptive learning systems within contemporary online learning design Professor.

FNA/Spring CENG 562 – Machine Learning. FNA/Spring Contact information Instructor: Dr. Ferda N. Alpaslan

Brain, Mind, and Computation Part III: Cognitive Brain Networks Brain, Mind, and Computation Part III: Cognitive Brain Networks Brain-Mind-Behavior Seminar.

Learning Styles and Teaching Bao Wenmin Anhui Institute of Educational Science.

Artificial Intelligence DNA Hypernetworks Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.

Bayesian Brain - Chapter 11 Neural Models of Bayesian Belief Propagation Rajesh P.N. Rao Summary by B.-H. Kim Biointelligence Lab School of.

Brief Intro to Machine Learning CS539

The 14th International Conference on Neural Information Processing (ICONIP2007) ICONIP 2007 참관기 홍진혁.

Machine Learning overview Chapter 18, 21

Machine Learning overview Chapter 18, 21

Chapter 11: Artificial Intelligence

Spring 2003 Dr. Susan Bridges

Done Done Course Overview What is AI? What are the Major Challenges?

6. Feed-forward mapping networks

Artificial Intelligence

Ch 14. Active Vision for Goal-Oriented Humanoid Robot Walking (1/2) Creating Brain-Like Intelligence, Sendhoff et al. (eds), Robots Learning from.

DNA Computing and Molecular Programming

Cognitive Learning and the Multimodal Memory Game: Toward Human-Level Machine Learning 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)

Artificial Intelligence (Lecture 1)

Chapter 10: The cognitive brain

Machine Learning overview Chapter 18, 21

Foundations of Inclusive Education

Morteza Kheirkhah University College London

Presentation transcript:

Teaching an Agent by Playing a Multimodal Memory Game: Challenges for Machine Learners and Human Teachers AAAI 2009 Spring Symposium: Agents that Learn from Human Teachers March 23-25, 2009, Stanford University Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering Cognitive Science, Brain Science, and Bioinformatics Seoul National University, Seoul , Korea

© 2009, SNU Biointelligence Lab, 2 Talk Outline Multimodal Memory Game (MMG) Challenges for Machine Learners Challenges for Human Teachers Toward Self-teaching Cognitive Agents

© 2009, SNU Biointelligence Lab, Image Sound Text But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. Image-to-Text Generator (I2T) Image-to-Text Generator (I2T) Text-to-Image Generator (T2I) Text-to-Image Generator (T2I) Text Hint But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. But, I'm getting married tomorrow Well, maybe I am... I keep thinking about you. And I'm wondering if we made a mistake giving up so fast. Are you thinking about me? But if you are, call me tonight. Hint Image Machine Learner Toward Human-Level Machine Learning: Multimodal Memory Game (MMG)

Image Sound © 2009, SNU Biointelligence Lab, 4 Text Generation Game (from Image) Text I2T Learning by Viewing Learning by Viewing T2I Game Manager Game Manager TextHint T

Text Image Sound © 2009, SNU Biointelligence Lab, 5 Image Generation Game (from Text) I2T Learning by Viewing Learning by Viewing T2I Game Manager Game Manager HintImage I

Characteristics of MMG Game Interactive Multimodal Long-lasting Hard to learn Scalable data Humans as teachers Difficulty controllable Learning by imitation (viewing and watching) © 2009, SNU Biointelligence Lab, 6

Three Approaches Learning Architecture  Model Learning Strategies  Algorithms Teaching Strategies  Humans © 2009, SNU Biointelligence Lab, 7

Methods of Machine Learning Symbolic Learning  Version Space Learning  Case-Based Learning Neural (Connectionist) Learning  Multilayer Perceptrons  Self-Organizing Maps  Hopfield Networks Evolutionary Learning  Evolution Strategies  Evolutionary Programming  Genetic Algorithms  Genetic Programming Probabilistic Learning  Bayesian Networks  Boltzmann Machines  Hidden Markov Models  Deep Belief Networks  Hypernetworks Other Machine Learning Methods  Reinforcement Learning  Decision Trees  Boosting Algorithms  Kernel Methods (SVM)  PCA, ICA, LDA etc.

Learning with Hypernetworks © 2009, SNU Biointelligence Lab, 9 x1 x2 x3 x4 x5 x6 x7 x8x9 x10 x11 x12 x13 x14 x15 [Zhang, DNA ]

© 2009, SNU Biointelligence Lab, 10 How to Learn from Image-Text Pairs

© 2009, SNU Biointelligence Lab, 11 How to Generate Image from Text

Image-to-Text Translation Results © 2009, SNU Biointelligence Lab, 12 AnswerQuery I don't know what happened There's a kitty in my guitar case Maybe there's something I can do to make sure I get pregnant Maybe there's something there's something I … I get pregnant There's a a kitty in … in my guitar case I don't know don't know what know what happened Matching & Completion

Text-to-Image Translation Results © 2009, SNU Biointelligence Lab, 13 Query Matching & Completion I don't know what happened Take a look at this There's a kitty in my guitar case Maybe there's something I can do to make sure I get pregnant Answer

Further Challenges

Challenges for Machine Learners Incremental learning Online learning Fast update One-shot learning Predictive learning Memory capacity Selective attention Active learning Context-awareness Persistency Concept drift Multisensory integration © 2009, SNU Biointelligence Lab, 15

Challenges for Human Teachers Getting feedback Sequencing examples Identifying the weak points Choosing problems Controlling parameters Evaluating progress Estimating difficulty Generating new queries Modeling the effect of learning parameters Catching environmental change Minimal interactions Multimodal interaction © 2009, SNU Biointelligence Lab, 16

Conclusion Multimodal memory game (MMG)  Highly-interactive lifelong learning scenario  Challenges current machine learning techniques Challenges for machine learners  More attentive, active behavior  Rather than parameter fitting, passive adaptation Human partners  More active role in interacting with the agents The future: Self-teaching cognitive agents  Cognitive learning agents that teach themselves = Active learning agents + cognitively-aware human teachers  Design new queries and test their answers by interacting with humans © 2009, SNU Biointelligence Lab, 17