From NARS to a Thinking Machine

Pei WangTemple University

Content

NARS (Non-Axiomatic Reasoning System): a project aimed at building a general-purpose intelligent system, or a “thinking machine”

• The main ideas behind the project• The development plan of the project• The past, present, and future of the project

Observations

• “Intelligence” is a capability possessed by human beings, but not by animals and ordinary computers.

• The major difference: not in “what it can do”, but in “what it can learn to do”.

• Key features: adaptivity, generality, creativity, flexibility, but not absolute optimity.

Methodology

• Minimalism: not to maximize the system’s performance, but to minimize its theoretical assumptions and technical instruments, while still achieving desired performance.

• There are scientific and engineering reasons for following such an approach.

• Many such attempts have failed, but they might have followed wrong ideas.

Basic Principle

“Intelligence” is the capability of a system to adapt to its environment and to work with insufficient knowledge and resources.

The system should• rely on constant processing capacity,• work in real time,• open to unexpected tasks,• learn from experience.

Framework

• NARS is built within the framework of a reasoning system, with a language for knowledge representation, a semantics of the language, a set of inference rules, a memory architecture, a control mechanism.

• Advantages: being domain-independent, combining the justifiability of individual steps and the flexibility of processes.

Categorical Language

• A typical sentence: bird animal [1.0, 0.9]• Term: “bird” and “animal” are names of

concepts• Inheritance relation “” : special-general• Truth value: [frequency, confidence]

Experience-Grounded Semantics

• The truth value of a sentence is determined by available evidence in the experience:

f = w+/w, c = w/(w+1)• Truth value uniformly represents

randomness, fuzziness, and ignorance.• The meaning of a term is defined by its

experienced relations with other terms.

Basic Inference Rules

S P

S

M

P

abduction

S

M

P

M

S P

deduction

induction revision

Memory as a Belief Network

bird

gull

swan

robinswimmer

crow

feathered_creature

[1.00, 0.90] [1.00, 0.90]

[0.00

, 0.90

][1.00, 0.90]

[1.00, 0.90] [1.00, 0.90][1.00, 0.90]

[1.00, 0.90]

Cbird

Control Strategy

• In each step, a task is processed by interacting with a belief, according to certain rules.

• The task and belief are selected probabilistically, according to priority distributions among related tasks and beliefs.

• Factors influence the priority of an item: quality of the item, usefulness of the item in history, and relevance of the item to the current context.

Compound Terms

• Compound terms: sets, intersections, differences, products, and images.

• Variants of the inheritance relation: similarity, instance, and property.

• New inference rules are added to carry out compound composition and decomposition.

• Related changes in memory and control.

Higher-Order Reasoning

• Two higher-order relations, implication and equivalence, are defined between statements.

• Compound statements: negations, conjunctions, and disjunctions.

• The implication relation is used to carry out conditional and hypothetical inferences.

• Variable terms are used to carry out general and abstract inferences.

Procedural Reasoning

• Events as statements with temporal relations (sequential and parallel). Prediction and explanation as temporal inferences.

• Operations as statements with procedural interpretation. Skill learning and planning as procedural inferences.

• Goals as statements to be realized. Decision making as the making of new goals.

Development Progress

• DONE: language definitionsemantics specificationbasic inference rules

rules for compound terms rules for higher-order inference basic memory and control• DOING: rules for temporal/procedural inference

refined memory and control

NARS Plus

Optional extensions of NARS:• sensorimotor interface• natural language interface• education procedure• socialization procedure• special hardware• evolution process

Conclusions

• An AI system should follow the same principles as the human mind, though it may have different internal structure, external behavior, practical ability, etc.

• To see intelligence as “adaptation with insufficiency” explains mental processes, guides system design, and distinguishes AI from other disciplines.

Information about NARS

• Website: containing 30+ publications and on-line demonstrations (a Java Applet and a Prolog program) of NARS (Version 4.2). (http://www.cogsci.indiana.edu/farg/peiwang/papers.html)

• Book: Rigid Flexibility: The Logic of Intelligence, Springer, ISBN 1402050445, Available: September 15, 2006. (Wang-Contents-Preface.pdf)