Selected Papers

This page is no longer actively maintained. Instead, please visit http://www.cis.temple.edu/~pwang/papers.html

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

• Rationality-guided AGI as cognitive systems (by Ahmed Abdel-Fattah, Tarek R. Besold, Helmar Gust, Ulf Krumnack, Martin Schmidt, Kai-Uwe Kuhnberger, and Pei Wang)
  [Proceedings of CogSci 2012, Sapporo, Japan, August 2012]
  New models of rationality and their relation with AGI and cognitive science
• 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. An extended version is published as a journal article in 2011.]
  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

• On the validity of Dempster-Shafer theory (by Jean Dezert, Pei Wang, and Albena Tchamova)
  [Proceedings of Fusion 2012, Singapore, July 2012]
  Dempster rule is conjunctive, while evidence combination should be additive
• 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
  [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
  [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

• The frame problem, the relevance problem, and a package solution to both (by Yingjin Xu and Pei Wang)
  [Synthese, DOI: 10.1007/s11229-012-0117-8, 2012]
  How NARS handles semantic relevance
• 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

Application

• Reasoning in Non-Axiomatic Logic: A Case Study in Medical Diagnosis (by Pei Wang and Seemal Awan)
  [Proceedings of AGI-11, 297-302, Mountain View, California, August 2011]
  Testing the expressive and inferential power of NAL in medical diagnosis
• Recommendation Based on Personal Preference
  [Computational Web Intelligence: Intelligent Technology for Web Applications, 101-115, World Scientific Publishing Company, 2004]
  Applying the evidence measurement of NARS in recommendation and on-line shopping