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Transcript
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
Vehicle Intelligence Laboratory
School of Electrical and Computer Engineering
Seoul National University
http://vi.snu.ac.kr
Contents

Introduction

The Seduction of Embodiment

Fallacies in Nouvelle AI

Limitations of Symbolic AI

Meta-semantic and Exosomatic Ontologies

Morphology and Development
2
Contents


Requirements for Human Visual Processing

Two Extreme Kinds of Dynamical System

Topics for Further Investigation
The Variety of Possible Architectures

Dynamical Systems for Symbolic AI

Types of Architecture

Layered Perception and Action

How Evolution Produced Mathematicians?

Conclusion
3
Some Questions
 What animals did you see?
 What was in the last picture?
 What was in the picture taken at dusk?
 Did you see any windows?
 Did you see a uniformed official?
 Did anything have horns? What?
 Did anything have hands on the floor?
Requirements for
Human Visual Processing
 Human process photographs
at a rate of one per second.
 No known mechanism exists
for human visual processing.
 Reflection on a wide range of phenomena has led to
a hypothesized architecture with a complex system.
■
Two Extreme Kinds of
Dynamical System
 a simpler one
 assumed to build
“biologically inspired” robots
 key feature is close coupling
between internal states and
the environment
1 / 26
Two Extreme Kinds of
Dynamical System
 more complex one
Topics for further investigation
 how to build dynamical systems
 how those systems are constructed over many years
 how the constraint-propagation works
 how the processes are influenced by various aspects
 how the resulting percepts continue to be driven
 how all of that is used
The Variety of Possible Architectures
Reminder

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
The Variety of Possible Architectures:
Dynamical Systems for Symbolic AI
 Existing symbolic AI systems are clearly nowhere near
human competence except in very narrow domains:
 chess (not Go), Mathematica, Matlab, compiler optimization, …
 Symbolic AI will suffice for everything often fail to attend
to the kinds of intelligence required for controlling continuous
actions in a 3-D structured environment:
 maintaining balance while pushing a broom,
drawing a picture with a pencil, …
The Variety of Possible Architectures:
Dynamical Systems for Symbolic AI
 Nevertheless,
it should be clear that the “traditional”
sense→think/decide→act loop
(presented in several AI textbooks) is
much too restrictive to accommodate
the requirements presented before.
The Variety of Possible Architectures:
Types of Architecture
 The CogAff schema
The Variety of Possible Architectures:
Types of Architecture
 The H-CogAff architecture
The Variety of Possible Architectures:
Layered Perception and Action
 Vision operates at different levels of abstraction:
How Evolution Produced
Mathematicians?
How Evolution Produced
Mathematicians?
How Evolution Produced
Mathematicians?
 From Empirical to Mathematical Truths
 turning around, counting objects, rubber band, …
 Empirical discovery becomes a necessary truth.
 It’s not a matter of probabilities.
 different ways can be exist for human
to discover useful affordances
Conclusion
 Much of the work on embodied cognition in robots
has focused on the terribly narrow problem of learning.
 Symbolic computation was a human
competence long before AI began, so
there must be biological mechanisms
that make it possible.
Conclusion
 It will be necessary for AI researchers to abandon
factional disputes and stop claiming of one key mechanism.
 A synthesis of complementary approaches
might be essential to progress in the long run
■
Q&A
26 / 26
References
 인공지능은 뇌를 닮아 가는가, 유신 저, 컬처룩, 2014
 Aaron Sloman, “Some Requirements for Human-Like Robots:
Why the Recent Over-Emphasis on Embodiment Has Held Up
Progress”, Creating Brain-Like Intelligence, pp. 248-277,
Springer (2009)
 “Artificial Intelligence”, Wikipedia, Sep. 2015,
https://en.wikipedia.org/wiki/Artificial_intelligence