Chapter 12. Some Requirements for Human-Like Robots: Why the Recent Over-Emphasis on Embodiment Has Held Up Progress Aaron Sloman School of Computer Science,

Slides:



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

Cognitive Systems, ICANN panel, Q1 What is machine intelligence, as beyond pattern matching, classification and prediction. What is machine intelligence,
ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE SYSTEMS
Artificial Intelligence Created by Korbut Fyodor FTF,
5-1 Chapter 5: REACTIVE AND HYBRID ARCHITECTURES.
Artificial Intelligence. Intelligent? What is intelligence? computational part of the ability to achieve goals in the world.
Artificial Intelligence
AI 授課教師:顏士淨 2013/09/12 1. Part I & Part II 2  Part I Artificial Intelligence 1 Introduction 2 Intelligent Agents Part II Problem Solving 3 Solving Problems.
A Brief History of Artificial Intelligence
WHAT IS ARTIFICIAL INTELLIGENCE?
Will Androids Dream of Electric Sheep? A Glimpse of Current and Future Developments in Artificial Intelligence Henry Kautz Computer Science & Engineering.
Matching brain and body dynamics Daniel Wolpert: – "Why don't plants have brains?" – "Plants don't have to move!" Early phases of embodied artificial intelligence:
COMP 4640 Intelligent & Interactive Systems Cheryl Seals, Ph.D. Computer Science & Software Engineering Auburn University.
Chapter 4: Towards a Theory of Intelligence Gert Kootstra.
1 Chapter 4 Decision Support and Artificial Intelligence Brainpower for Your Business.
Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Cognitive Robots © 2014, SNU CSE Biointelligence Lab.,
Mobiles Robotics: Integrated Systems Design. Where are the Robots? Exploration.
CPSC 171 Artificial Intelligence Read Chapter 14.
Artificial Intelligence By Ryan Shoultes & Jeremy Creighton.
ARTIFICIAL INTELLIGENCE Introduction: Chapter 1. Outline Course overview What is AI? A brief history The state of the art.
CISC4/681 Introduction to Artificial Intelligence1 Introduction – Artificial Intelligence a Modern Approach Russell and Norvig: 1.
Introduction: Chapter 1
COMP 4640 Intelligent & Interactive Systems Cheryl Seals, Ph.D. Computer Science & Software Engineering Auburn University Lecture 2: Intelligent Agents.
10/3/2015 ARTIFICIAL INTELLIGENCE Russell and Norvig ARTIFICIAL INTELLIGENCE: A Modern Approach.
A RTIFICIAL I NTELLIGENCE Introduction 3 October
Artificial Intelligence
Artificial Intelligence Introductory Lecture Jennifer J. Burg Department of Mathematics and Computer Science.
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.
Beyond Gazing, Pointing, and Reaching A Survey of Developmental Robotics Authors: Max Lungarella, Giorgio Metta.
SEMINAR REPORT ON K.SWATHI. INTRODUCTION Any automatically operated machine that functions in human like manner Any automatically operated machine that.
Synthetic Cognitive Agent Situational Awareness Components Sanford T. Freedman and Julie A. Adams Department of Electrical Engineering and Computer Science.
George F Luger ARTIFICIAL INTELLIGENCE 6th edition Structures and Strategies for Complex Problem Solving Artificial Intelligence as Empirical Enquiry Luger:
1 CS 2710, ISSP 2610 Foundations of Artificial Intelligence introduction.
Intelligent Robotics An Introduction The King’s Academy November 2, 2007.
Mobiles Robotics: Integrated Systems Design. Where are the Robots? Exploration.
Course Instructor: K ashif I hsan 1. Chapter # 1 Kashif Ihsan, Lecturer CS, MIHE2.
Chapter 1 The Product Software is
Physical Science Chapter 1. What is science? Science is the study of… Science is the study of… Everything!! Everything!! The goal is to try and make the.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 25 –Robotics Thursday –Robotics continued Home Work due next Tuesday –Ch. 13:
Chapter 7. Learning through Imitation and Exploration: Towards Humanoid Robots that Learn from Humans in Creating Brain-like Intelligence. Course: Robots.
Definitions of AI There are as many definitions as there are practitioners. How would you define it? What is important for a system to be intelligent?
Chapter 1. Cognitive Systems Introduction in Cognitive Systems, Christensen et al. Course: Robots Learning from Humans Park, Sae-Rom Lee, Woo-Jin Statistical.
Chapter 10. The Explorer System in Cognitive Systems, Christensen et al. Course: Robots Learning from Humans On, Kyoung-Woon Biointelligence Laboratory.
Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-like Systems, Sendhoff et al. Course: Robots Learning from Humans Baek, Da Som.
VEHICLE INTELLIGENCE LAB
Chapter 12. Some Requirements for Human-Like Robots in Creating Brain-Like Intelligence, Aaron Solman. Course: Robots Learning from Humans Hur, Woo-Sol.
FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
RULES Patty Nordstrom Hien Nguyen. "Cognitive Skills are Realized by Production Rules"
Artificial Intelligence: Research and Collaborative Possibilities a presentation by: Dr. Ernest L. McDuffie, Assistant Professor Department of Computer.
Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-Like Intelligence, Olaf Sporns Course: Robots Learning from Humans Park, John.
Chapter 1. Introduction in Creating Brain-like intelligence, Sendhoff et al. Course: Robots Learning from Humans Bae, Eun-bit Otology Laboratory Seoul.
COMP 4640 Intelligent & Interactive Systems Cheryl Seals, Ph.D. Computer Science & Software Engineering Auburn University.
Chapter 4. Analysis of Brain-Like Structures and Dynamics (2/2) Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans 09/25.
Science and Engineering Practices K–2 Condensed Practices3–5 Condensed Practices6–8 Condensed Practices9–12 Condensed Practices Developing and Using Models.
COMPUTER SYSTEM FUNDAMENTAL Genetic Computer School INTRODUCTION TO ARTIFICIAL INTELLIGENCE LESSON 11.
Overview of Artificial Intelligence (1) Artificial intelligence (AI) Computers with the ability to mimic or duplicate the functions of the human brain.
CSC 290 Introduction to Artificial Intelligence
BASIC ELECTRONICS & ROBOTICS Instructor: Humayun Rashid Raahat
Artificial Intelligence (CS 370D)
Seven Principles of Synthetic Intelligence
Bioagents and Biorobots David Kadleček, Michal Petrus, Pavel Nahodil
COMP 4640 Intelligent & Interactive Systems
Course Instructor: knza ch
Introduction Artificial Intelligent.
Foundations for Algebra
Course Outline Advanced Introduction Expert Systems Topics Problem
CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano
CS 404 Artificial Intelligence
Artificial Intelligence
Presentation transcript:

Chapter 12. Some Requirements for Human-Like Robots: Why the Recent Over-Emphasis on Embodiment Has Held Up Progress Aaron Sloman School of Computer Science, University of Birmingham Course: Robots Learning from Humans DooSan Baek Vehicle Intelligence Laboratory School of Electrical Engineering Seoul National University

Contents Introduction The Seduction of Embodiment Fallacies in Nouvelle AI Limitations of Symbolic AI Meta-semantic and Exosomatic Ontologies Morphology and Development 2

Introduction There are important limitations in current ideas about embodiment and dynamical systems Only considers sense-think-act cycle Rejects symbolic AI Their failure is mainly because.. Problems were not understood Not that the wrong mechanisms were adopted 3

The Seduction of Embodiment Symbolic AI Symbol grounding Kant’s empiricism Learning of robots to discovery of statistical patterns relating sensory and motor signals Nouvelle AI (Brook) Emphasis embodiment and sensory-motor interactions with the environment Dispense with symbolic representations (But not fully rejected) Use morphology to reduce the SW sophistication 4

The Seduction of Embodiment Morphology Can be Important Simplify problems of grasping and manipulation of 3D objects Recent work on embodied robots has been impressive BigDog, Asimo While still failing Plan future actions Reason about unobserved events Represent mental states of others 5

Fallacies in Nouvelle AI The Failure to Identify Deliberative Requirements Ex) Elephant The immediate environment may be a good source But it is not suitable for complex decision making Elephant may not play chess, solve algebra problem Simple but sophisticated framework is needed “Fully deliberative” 6

Fallacies in Nouvelle AI Fully deliberative Construct several representations of branching futures Represent and compare their relative strengths and weakness Select one as a plan and use it to generate behaviors While being prepared to learn from mistaken decisions of that sort 7

Limitations of Symbolic AI The early symbolic AI Did not take account sufficient uncertainty and error Did not consider actions that causing unreliable results Slippage, wear and tear 8

Meta-semantic and Exosomatic Ontologies Meta-semantic Robot should understand other intelligent agent’s actions Robot should infer other’s mental states Exosomatic Ontology For sophisticated animals Allows generalizations For perceiving a grasping One hand -> other hand, two hand, other agents 9

Morphology and Development Morphology and perception Morphology is less relative with the ability of environment perception Thalidomide babies, SLAM Human can overcome deformity, illness 10