Seven Principles of Synthetic Intelligence

1. Seven Principles ofSynthetic Intelligence Joscha Bach Institute for Cognitive Science, Univ. of Osnabruk, Germany The 1st Conference on Artificial General Intelligence, 2008 김 민 경 2008. 4. 7

2. 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” Backgrounds

3. Outlines • Seven Principles of • Synthetic Intelligence • II. MicroPsi Overview

4. 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” Lessons for Synthesizing Intelligence

5. 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 mindby a functional structure (perception, learning, action selection & planning, memory, etc.) Complete & Integrated systems Perception – Deliberation – Emotion – Motivation – Learning … Lessons for Synthesizing Intelligence – (1)

6. 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 Lessons for Synthesizing Intelligence – (2,3)

7. 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 Lessons for Synthesizing Intelligence – (4)

8. Lessons for Synthesizing Intelligence – (4) Amodal Symbol Representation Modal 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. • 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.

9. 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 Lessons for Synthesizing Intelligence – (5,6)

10. 7. Intelligence is not going to simply “emerge” “Strong emergence” Intelligent mind including human specifics (social personhood, motivation, self-conceptualization) 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 Lessons for Synthesizing Intelligence – (7)

11. 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 • Informationabout 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.

12. MicroPsi architecture Urge 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 Motive = Urge + Goal Goals are memory schema that represent past satisfying situation Intention = Enhanced motive with plan, state, time and so on.

13. 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. • Perceiving something unexpected or unknown may increase the need for competence or certainty. s P C

14. 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.