selected papers

All the following publications are authored by Pei Wang unless specified otherwise.

The on-line versions here may be slightly different from the published versions.

Project Overview

A Logical Model of Intelligence - an introduction to NARS
[On-line document since 2009]
A brief overview of the project
From NARS to a Thinking Machine [book chapter, presentation abstract, PPT slides, and video]
[Artificial General Intelligence Research Institute Workshop, Washington DC, May 2006]
A discussion of the development plan of NARS
The Logic of Intelligence
[Artificial General Intelligence, 31-62, Springer, 2006]
A high-level description of the NARS project
Toward a Unified Artificial Intelligence
[AAAI Fall Symposium on Achieving Human-Level Intelligence through Integrated Research and Systems, 83-90, Washington DC, October 2004]
AI should, and can, be treated as a whole
Non-Axiomatic Reasoning System (Version 4.1)
[The Seventeenth National Conference on Artificial Intelligence, 1135-1136, Austin, Texas, July 2000]
A brief description of the NARS 4.1 demo, as exhibited in AAAI Intelligent Systems Demos

System Description

Rigid Flexibility: The Logic of Intelligence
[also as eBook, with freely accessible Front Matter and Back Matter, Springer, 2006]
A comprehensive description of the NARS project as of version 4.3
Non-Axiomatic Logic (NAL) Specification
[On-line document since 2009]
The logic of NARS, formally defined and up-to-date
Non-Axiomatic Reasoning System: Exploring the essence of intelligence
[Ph.D. Dissertation, Indiana University, 1995]
A comprehensive description of the NARS project as of version 3.0
Non-Axiomatic Reasoning System (Version 2.2)
[Technical Report No. 75 of CRCC, 1993]
A comprehensive description of the NARS project as of version 2.2

General Issues

The Evaluation of AGI Systems
[Proceedings of AGI-10, Lugano, Switzerland, March 2010]
Evaluation and meta-evaluation, empirical vs. theoretical
Insufficient knowledge and resources: a biological constraint and its functional implications
[Papers from AAAI 2009 Fall Symposium on Biologically Inspired Cognitive Architectures, Pages 88-93, Arlington, Virginia, November 2009]
The assumption on insufficient knowledge and resources is crucial for AI
Suggested Education for Future AGI Researchers
[On-line document since 2008]
The background knowledge needed for AGI research, a personal view
What Do You Mean by "AI"? [presentation]
[Proceedings of AGI-08, Pages 362-373, Memphis, Tennessee, March 2008]
Analysis and comparison of five typical ways to define AI
Artificial General Intelligence: A Gentle Introduction
[On-line document since 2007]
AGI: theoretical problems and representative answers
Aspects of Artificial General Intelligence (by Pei Wang and Ben Goertzel)
[Introduction chapter of Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, IOS Press, 2007]
Clarification and justification of AGI research in general
Three Fundamental Misconceptions of Artificial Intelligence
[Journal of Experimental & Theoretical Artificial Intelligence, 19(3), 249-268, 2007]
It is a mistake to always take an AI system as an axiomatic system, a Turing machine, or a system with a model-theoretic semantics
Artificial General Intelligence and Classical Neural Network
[The IEEE International Conference on Granular Computing, 130-135, Atlanta, Georgia, May 2006]
The strength and weakness of neural networks as general-purpose intelligent systems
Artificial Intelligence: What it is, and what it should be
[The AAAI Spring Symposium on Cognitive Science Principles Meet AI-Hard Problems, 97-102, Stanford, California, March 2006]
On the identity and methodology of AI
On the Working Definition of Intelligence
[Technical Report No. 94 of CRCC, 1994. The on-line version is a revision finished in 1995.]
The general philosophical issues of artificial intelligence

Logic and Reasoning

Analogy in a General-Purpose Reasoning System
[Cognitive Systems Research, 10(3), 286-296, 2009]
Comparing the analogy in NARS with that in Copycat and SME
Cognitive Logic versus Mathematical Logic
[The Third International Seminar on Logic and Cognition, Guangzhou, May 2004]
The logic of mathematics is not the logic of cognition
The Generation and Evaluation of Generic Sentences
[Philosophical Trends, Supplement 2004, 35-44]
Use NARS to handle generic sentences
Wason's Cards: What is Wrong?
[The Third International Conference on Cognitive Science, 371-375, Beijing, August 2001]
A comparison of NARS and traditional logic in terms of their conception of "evidence"
Abduction in Non-Axiomatic Logic
[The IJCAI Workshop on Abductive Reasoning, 56-63, Seattle, Washington, August 2001]
Introducing Higher-Order Non-Axiomatic Logic, and comparing it with other approaches on abduction
Unified Inference in Extended Syllogism
[Abduction and Induction, 117-129, Kluwer Academic Pub, 2000]
A unified formalization of deduction, induction, abduction, and revision as extended syllogism
A New Approach for Induction: From a Non-Axiomatic Logical Point of View
[Philosophy, Logic, and Artificial Intelligence, 53-85, Zhongshan University Press, 1999]
The induction capacity of NARS
From Inheritance Relation to Non-Axiomatic Logic
[International Journal of Approximate Reasoning, 11(4), 281-319, 1994]
A detailed description of the logical kernel of NARS

Uncertainty

Formalization of Evidence: A Comparative Study
[Journal of Artificial General Intelligence, 1, 25-53, 2009]
It takes two numbers to properly measure evidential support for a belief
The Limitation of Bayesianism
[Artificial Intelligence, 158(1), 97-106, 2004]
Bayesianism has no general rule to revise beliefs
Confidence as Higher-Order Uncertainty
[The Second International Symposium on Imprecise Probabilities and Their Applications, 352-361, Ithaca, New York, June 2001]
A discussion about the confidence measurement defined in NARS, and its relation with probability-based approaches
Heuristics and Normative Models
[International Journal of Approximate Reasoning, 14(4), 221-235, 1996]
How NARS can reproduce various "heuristics and biases" observed in human reasoning
The Interpretation of Fuzziness
[A revised version appears in IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(2), 321-326, 1996]
NARS vs. fuzzy logic
A Unified Treatment of Uncertainties
[A revised version appears in The Fourth International Conference for Young Computer Scientists, 462-467, Beijing, July 1995]
A general description about the uncertainty representation in NARS, including brief comparisons with other approaches
Reference Classes and Multiple Inheritances
[A revised version appears in International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 3(1), 79-91, 1995]
NARS vs. non-monotonic logics and probability theory
A Defect in Dempster-Shafer Theory
[The Tenth Conference of Uncertainty in Artificial Intelligence, 560-566, Seattle, WA, July 1994]
NARS vs. D-S theory
Belief Revision in Probability Theory
[The Ninth Conference of Uncertainty in Artificial Intelligence, 519-526, Washington DC, July 1993]
NARS vs. the Bayesian approach

Meaning and Truth

Embodiment: Does a laptop have a body?
[Proceedings of AGI-09, Pages 174-179, Arlington, Virginia, March 2009]
Being "embodiment" means to take experience into account
Experience-Grounded Semantics: A theory for intelligent systems
[Cognitive Systems Research, 6(4), 282-302, 2005]
To define "truth" and "meaning" according to experience

Categorization and Learning

The Logic of Categorization
[The Fifteenth FLAIRS Conference, 181-185, Pensacola, Florida, May 2002]
A discussion of the categorization model in NARS, which is integrated with reasoning and learning
The Logic of Learning
[The AAAI workshop on New Research Problems for Machine Learning, 37-40, Austin, Texas, July 2000]
A comparison of inference-based learning and algorithm-based learning
Comparing Categorization Models: A psychological experiment
[Technical Report No. 79 of CRCC, 1993]
Comparisons of NARS with some categorization models proposed by psychologists.

Resource Management

Case-by-case problem solving
[Proceedings of AGI-09, Pages 180-185, Arlington, Virginia, March 2009]
Directly solving a problem instance using available knowledge and resources
Computation and Intelligence in Problem Solving
[A revised version appears as "Problem solving with insufficient resources" in the International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 12(5), 673-700, 2004]
How to solve problems without problem-specific algorithms