Project Overview
- 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, exhibited in AAAI Intelligent Systems Demos.
System Description
General Issues
- Suggested Education for Future AGI Researchers
[recommended readings, 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
[talk outline, 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 general philosophical issues of artificial intelligence.
Logic and Reasoning
- 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
- 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
[IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(2), 321-326, 1996.]
NARS vs. fuzzy logic.
- A Unified Treatment of Uncertainties
[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
[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
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
Application