My later publications and demonstrations have been moved to http://sites.google.com/site/narswang/.

- "Non-Axiomatic Reasoning System --- Exploring the essence of intelligence", my Ph.D. dissertation, contains a complete description of the NARS project (version 3.0).
- "On the Working Definition of Intelligence" is about the general philosophical and methodological issues of artificial intelligence. This paper sets a foundation for the whole NARS project.
- "From Inheritance Relation to Non-Axiomatic Logic" is a detailed description about the logical kernel of NARS.
- "Grounded on Experience: Semantics for intelligence" is a discussion on the semantics of NARS.
- "A New Approach for Induction: From a Non-Axiomatic Logical Point of View" focuses on the induction capacity of NARS.
- "Problem-Solving under Insufficient Resources" describes the control mechanism and memory structure of NARS.
- "Non-Axiomatic Reasoning System (Version 2.2)" is a complete but brief description of a previous version of NARS.

- "A Unified Treatment of Uncertainties" is a general description about the uncertainty representation in NARS, including brief comparisons with other approaches.
- "Belief Revision in Probability Theory": NARS vs. Bayesian approach.
- "The Interpretation of Fuzziness": NARS vs. fuzzy logic.
- "A Defect in Dempster-Shafer Theory": NARS vs. D-S theory.
- "Confidence as Higher-Order Uncertainty": NARS vs. higher-order probability.
- "Reference Classes and Multiple Inheritances": NARS vs. non-monotonic logics and probability theory.

- "Heuristics and Normative Models" shows how NARS can reproduce various "heuristics and biases" observed in human reasoning.
- "Comparing Categorization Models --- A psychological experiment" compares NARS with some categorization models proposed by psychologists.

The dissertation has 181 pages. It is available as *.ps file(5M), *.ps.zip file(460K), and *.ps.gz file (430K).

**Abstract:**

Every artificial-intelligence research project needs a working definition of ``intelligence'', on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, ``intelligence'' is defined as the capacity to adapt under insufficient knowledge and resources. Concretely, an intelligent system should be finite and open, and should work in real time.

If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system.

NARS uses a term-oriented formal language, characterized by the use of subject--predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several different types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way.

The inference rules of NARS are based on three inheritance relations between terms. With different combinations of premises, revision, deduction, induction, abduction, exemplification, comparison, and analogy can all be carried out in a uniform format, the major difference between these types of inference being that different functions are used to calculate the truth value of the conclusion from the truth values of the premises.

Since it has insufficient space--time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a ``controlled concurrency'' control mechanism, and a ``bag-based'' memory organization.

A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning.

This research sheds light on several notions in artificial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence.

Finally, the major results of the research are summarized, a preliminary evaluation of the working definition of intelligence is given, and the limitations and future extensions of the research are discussed.

**Abstract: **Non-Axiomatic Reasoning System (NARS) is designed to
be a general-purpose intelligent reasoning system, which is adaptive and
works under insufficient knowledge and resources. This paper focuses on
the components of NARS that contribute to the system's induction capacity,
and shows how the traditional problems in induction are addressed by the
system. The NARS approach of induction uses an term-oriented formal language
with an experience-grounded semantics that consistently interprets various
types of uncertainty. An induction rule generates conclusions from common
instance of terms, and a revision rule combines evidence from different
sources. In NARS, induction and other types of inference, such as deduction
and abduction, are based on the same semantic foundation, and they cooperate
in inference activities of the system. The system's control mechanism makes
knowledge-driven, context-dependent inference possible.

**Abstract: **This paper discusses the problem of resources-limited
information processing. After a review of relevant approaches in several
fields, a new approach, controlled concurrency, is described and analyzed.
This method is proposed for adaptive systems working under insufficient
knowledge and resources. According to this method, a problem-solving activity
consists of a sequence of steps which behaves like an anytime algorithm
--- it is interruptible, and the quality of the result is improved incrementally.
The system carries out many such activities in parallel, distributes its
resources among the them in a time-sharing manner, and dynamically adjusts
the distribution according to the feedback of each step. The step sequence
for a given problem is formed at run time, according to the system's knowledge
structure, which is also dynamically formed and adjusted. Finally, this
approach is compared with other approaches, and several of its properties
and implications are discussed.

**Abstract: **Model-theoretic semantics is inappropriate for adaptive
systems working with insufficient knowledge and resources. An experience-grounded
semantics is introduced in this paper, using NARS, an intelligent reasoning
system, as a concrete example. In NARS, the truth value of a sentence indicates
the amount of available evidence, and the meaning of a term indicates its
experienced relationship with other terms. Accordingly, both truth value
and meaning are dynamic and subjective. This approach provides new ideas
to the solution of some important problems in artificial intelligence.

**Abstract: **This paper is about the philosophical and methodological
foundation of artificial intelligence (AI). After discussing what is a
good ``working definition'', ``intelligence'' is defined as ``the ability
for an information processing system to adapt to its environment with insufficient
knowledge and resources''. Applying the definition to a reasoning system,
we get the major components of Non-Axiomatic Reasoning System (NARS), which
is a symbolic logic implemented in a computer system, and has many interesting
properties that closely related to intelligence. The definition also clarifies
the difference and relationship between AI and other disciplines, such
as computer science. Finally, the definition is compared with other popular
definitions of intelligence, and its advantages are argued.

**Abstract: **With insufficient knowledge, the conclusions made by
a reasoning system are usually uncertain. If the system is open to new
knowledge, it also suffers from a higher order uncertainty, because the
first order uncertainty evaluations are uncertain themselves --- they can
be changed by future evidence. Several approaches have been proposed for
handling higher order uncertainty, including the Bayesian approach, higher-order
probability, and so on. Though each of them has its advantages, none of
them is satisfactory, for various reasons. A new measurement, confidence,
is defined to indicate higher order uncertainty, which is understood as
relative stability of first order uncertainty evaluation, and is processed
accordingly.

**Abstract: **Evidence has be collected that probability theory is
not a proper descriptive model of intuitive human judgment. Some heuristics
have been proposed as such a descriptive model. This paper argues that
probability theory has limitations even as a normative model. A new normative
model of judgment under uncertainty is introduced, by which some heuristics
can be justified from a set of basic assumptions about intelligence.

**Abstract: **The reference class problem in probability theory and
the multiple inheritances (extensions) problem in non-monotonic logics
can be referred to as special cases of conflicting beliefs. The current
solution accepted in the two domains is the specificity priority principle.
By analyzing an example, several factors (ignored by the principle) are
found to be relevant to the priority of a reference class. A new approach,
Non-Axiomatic Reasoning System (NARS), is discussed, where these factors
are all taken into account. It is argued that the solution provided by
NARS is better than the solutions provided by probability theory and non-monotonic
logics.

**Abstract: **This paper begins by defining an environment of reasoning
under uncertainty, then it is shown why some existing approaches cannot
be used in such a situation. A new approach, Non-Axiomatic Reasoning System,
is introduced, which is built under the assumption that the system's knowledge
and resources are usually insufficient to handle the tasks imposed by the
environment. The system can consistently represent several types of uncertainty,
and can carry out multiple operations on these uncertainties. Finally,
the new approach is compared with the previous approaches in terms of uncertainty
representation and interpretation.

**Abstract: **By analyzing related issues in psychology and linguistics,
two basic types of fuzziness can be attributed to similarity and relativity,
respectively. In both cases, it is possible to interpret grade of membership
as the proportion of positive evidence, so as to treat fuzziness and randomness
uniformly.

**Abstract: **By analyzing the relationships among chance, weight
of evidence and degree of belief, we show that the assertion ``probability
functions are special cases of belief functions'' and the assertion ``Dempster's
rule can be used to combine belief functions based on distinct bodies of
evidence'' together lead to an inconsistency in Dempster-Shafer theory.
To solve this problem, we must reject some fundamental postulates of the
theory. We introduce a new approach for uncertainty management that shares
many intuitive ideas with D-S theory, while avoiding this problem.

**Abstract: **Non-Axiomatic Reasoning System is an adaptive system
that works with insufficient knowledge and resources. At the beginning
of the paper, three binary term logics are defined. The first is based
only on an inheritance relation. The second and the third suggest a novel
way to process extension and intension, and they also have interesting
relations with Aristotle's syllogistic logic. Based on the three simple
systems, a Non-Axiomatic Logic is defined. It has a term-oriented language
and an experience-grounded semantics. It can uniformly represents and processes
randomness, fuzziness, and ignorance. It can also uniformly carries out
deduction, abduction, induction, and revision.

**Abstract: **In this paper, a psychological experiment is proposed
to compare different theories on the internal structure of categories:
classical theory, prototype theory, exemplar theory, and NARS.

**Abstract: **Non-Axiomatic Reasoning System (NARS) is an intelligent
reasoning system, where intelligence means working and adapting with insufficient
knowledge and resources. NARS uses a new form of term logic, or an extended
syllogism, in which several types of uncertainties can be represented and
processed, and in which deduction, induction, abduction, and revision are
carried out in a unified format. The system works in an asynchronously
parallel way. The memory of the system is dynamically organized, and can
also be interpreted as a network. After present the major components of
the system, its implementation is briefly described. An example is used
to show how the system works. The limitations of the system are also discussed.

**Abstract: **In a probability-based reasoning system, Bayes' theorem
and its variations are often used to revise the system's beliefs. However,
if the explicit conditions and the implicit conditions of probability assignments
are properly distinguished, it follows that Bayes' theorem is not a generally
applicable revision rule. Upon properly distinguishing belief revision
from belief updating, we see that Jeffrey's rule and its variations are
not revision rules, either. Without these distinctions, the limitation
of the Bayesian approach is often ignored or underestimated. Revision,
in its general form, cannot be done in the Bayesian approach, because a
probability distribution function alone does not contain the information
needed by the operation.