Download PPT

Seven Principles of
Synthetic Intelligence
Joscha Bach
Institute for Cognitive Science, Univ. of Osnabruk, Germany
The 1st Conference on Artificial General Intelligence, 2008
김민경
2008. 4. 7
Slide #1
Backgrounds
AI
Success & Failure


(+) As a method of engineering

(-) As a method of understanding and superseding human intelligence and mind
Artificial General Intelligence


From the idea of studying “intelligence per se”
Slide #2
Outlines
I.
Seven Principles of
Synthetic Intelligence
II.
MicroPsi Overview
Slide #3
Lessons for Synthesizing Intelligence
1.
2.
3.
4.
5.
6.
7.
Build whole functionalist architecture
Let the question define the method
Aim for the Big Picture, not narrow solutions
Build grounded systems
Do not wait for robots to provide embodiment
Build autonomous systems
Intelligence is not going to simply “emerge”
Slide #4
Lessons for Synthesizing Intelligence – (1)
1. Build whole functionalist architecture
Functionalist Architecture

What entities we are going to research
 What constitutes these entities conceptually
 How we capture these concepts
Ex) About emotion…(“anger”, “pity”)
– Not by simply introducing a variable named “anger” or “pity”
– What exactly constitutes anger and pity within a cognitive agent system.
– Replace concepts underlying intelligence and mind by a functional structure
(perception, learning, action selection & planning, memory, etc.)

Complete & Integrated systems


Perception – Deliberation – Emotion – Motivation – Learning …
Slide #5
Lessons for Synthesizing Intelligence – (2,3)
2. Avoid methodologism; let the question define the method

Tools (graph theory, fuzzy logic, description languages, learning
paradigms, etc.) are like hammers that make everything look like a nail

Need to ask questions and find methods to answer them
3. Aim for the Big Picture, not narrow solutions


Understanding of intelligence have to be based on the integration of research
of the cognitive sciences
The study of AGI aims at a unified theory, and such a theory is going to be the
product of integration rather than specialization
Slide #6
Lessons for Synthesizing Intelligence – (4)
4. Build grounded systems

Restricted AI to micro-domains (sufficiently described using simple
ontologies and binary predicate logics)
 Failure to capturing richer and more heterogeneous domains
 Opened many eyes to Symbol Grounding Problem
(How to make symbols used by an AI system refer to the “proper meaning”)
*** The symbol grounding problem (Harnad, 1990)



The Chinese room (Searle, 1980)
Cognition cannot be just symbol manipulation
AI systems will have to be perceptual symbol systems, as opposed to
amodal symbol systems
Slide #7
Lessons for Synthesizing Intelligence – (4)
Amodal Symbol Representation


Perceptual states are transduced
into a completely new
representational system that
describes these states amodally.
The internal structure of these
symbols is unrelated to the
perceptual state that produced them.
Modal Symbol Representation


Subsets of perceptual states in
sensory-motor systems are extracted.
The internal structure of these
symbols is therefore
 modal and
 analogically related to the
perceptual state that produced
them.
Slide #8
Lessons for Synthesizing Intelligence – (5,6)
5. Do not wait for robots to provide embodiment

“A little robot stretching its legs” is intelligence ?

The level of intelligence of a critter is not measured by the number of its
extremities, but by its capabilities for representing, anticipating and
acting on its environment; “not by its brawns but by its brains”
6. Build autonomous systems
General intelligence




Both (1) to reach a given goal and (2) to set novel goals, “Exploration”
The Motivation to perform any action arises from a motivational system not from
intelligence itself
Every purposeful action of the system corresponds to one of its demands
Slide #9
Lessons for Synthesizing Intelligence – (7)
7. Intelligence is not going to simply “emerge”

“Strong emergence”
Intelligent mind including human specifics (social personhood, motivation, selfconceptualization) are the result of non-decomposable intrinsic properties of
interacting biological neurons, or of some non-decomposable process between
brains and the physical world

“Weak emergence”
The relationship between two modes of description
(a state of a computer program – the electric patterns in the circuits of the same computer)

Emergent processes are not going to “make intelligence appear” in an
information processing system of sufficient complexity. We still need to
implement the functionality that amounts to AGI into our models
Slide #10
MicroPsi architecture
•
•
•
MicroPsi is attempt to embody the seven principles of SI.
MicroPsi is an implementation of Dietrich Dörner’s Psi theory.
Psi Theory : A Model for Human Behavior
 Schema
• Information about the world is encoded in neurons,
so called quads.
• Hierarchical/temporal organization
• HyPercept (Hypothesis-driven Perception)
Bottom-up: low-level stimuli trigger those schema
hypotheses they have part in.
Top-down: hypotheses attempt to get their additional
elements to confirm further SUB-hypotheses or to reject
the current hypothesis.
Slide #11
MicroPsi architecture
 Urge
•
•
•

Motive = Urge + Goal
•

Water/Energy/Intactness: physiological urges, stream engine
Affiliation: social urge, accepted as a legitimate member(L-signal)
Certainty/Competence: cognitive urges, reliably predict, satisfying action
Goals are memory schema that represent past satisfying situation
Intention = Enhanced motive with plan, state, time and so on.
Slide #12
Psi Architecture
Psi Theory emphasizes the
integration of perception,
emotion, cognition, motivation
and action for human action
regulation
• Not focusing on single modules but
emphasizing the interaction of the
different components.
•
<In more detail…>
The Level of Contents: Measure for
the satisfaction of a certain need.
• The level of a need changes through
consumption (water, energy) or
perception.
•
•
P
s
C
Perceiving something unexpected or
unknown may increase the need for
competence or certainty.
Slide #13
MicroPsi Framework
•
•
•
•
Building agents according to the Psi theory.
Performing neural learning using hybrid representations.
Evolving motivational parameter settings in an artificial life environment.
Implementing a robotic control architecture using MicroPsi.
Slide #14