Is there an “essence of intelligence ” that distinguishes intelligent systems from non-intelligent systems? If there is, then what is it? This chapter suggests an answer to these questions by introducing the ideas behind the NARS (Non-Axiomatic Reasoning System) project. NARS is based on the opinion that the essence of intelligence is the ability to adapt with insufficient knowledge and resources. According to this belief, the author has designed a novel formal logic, and implemented it in a computer system. Such a“logic of intelligence ” provides a unified explanation for many cognitive functions of the human mind, and is also concrete enough to guide the actual building of a general purpose “thinking machine”. 1 Intelligence and Logic 1.1 To define intelligence The debate on the essence of intelligence has been going on for decades, and there is still little sign of consensus (this book itself is a piece of evidence). In the “mainstream AI”, the followings are some representative opinions: “AI is concerned with methods of achieving goals in situations in which the information available has a certain complex character. The methods that have to be used are related to the problem presented by the situation and are similar whether the problem solver is human, a Martian, or a computer program. ” [McCarthy, 1988] Intelligence usually means “the ability to solve hard problems”.

"Uncertainty in artificial intelligence" is an active research field, where several approaches have been suggested and studied for dealing with various types of uncertainty. However, it's hard to rank the approaches in general, because each of them is usually aimed at a special application environment. This paper begins by defining such an environment, then show why some existing approaches cannot be used in such a situation. Then a new approach, Non-Axiomatic Reasoning System, is introduced to work in the environment. The system is designed under the assumption that the system's knowledge and resources are usually insufficient to handle the tasks imposed by its 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. 1 The Problem The central issue of this paper is uncertaint...

To integrate existing AI techniques into a consistent system, an intelligent core is needed, which is general and flexible, and can use the other techniques as tools to solve concrete problems. Such a system, NARS, is introduced. It is a general-purpose reasoning system developed to be adaptive and capable of working with insufficient knowledge and resources. Compared to traditional reasoning system, NARS is different in all major components (language, semantics, inference rules, memory structure, and control mechanism). Intelligence as a whole Artificial intelligence started as an attempt to build a general-purpose thinking machine with human-level intelligence. In the past decades, there were projects aimed at algorithms and architectures capturing the essence of intelligence, such as General Problem Solver (Newell and Simon,

The AI system NARS contains a categorization model, in the sense that categorization and reasoning are two aspects of the same mechanism. As a theory of categorization, the NARS model unifies several existing theories. In this paper, the logic used in NARS is briefly described, and the categorization model is compared with the others.

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. 1 Introduction The term "induction" is usually used to denote the infe...

This research focuses on the development of an inference mechanism based on a particular variety of non-axiomatic systems known as Ontological Semantics. Systems with a heavy semantics emphasis and dynamic learning capabilities indicate a greater potential in inference-related applications, largely due to the structure of the resources which allows implementation of mixed methods: traditional deduction as well as framework-specific methods. An example illustrating the flow of the inference procedure is provided for clarity. 1

This paper is about the philosophical and methodological foundation of arti cial intelligence (AI). After discussing what is a good \working de nition", \intelligence " is de ned as \the ability for an information processing system to adapt to its environment with insu cient knowledge and resources". Applying the de nition to a reasoning system, we get the major components of Non-Axiomatic Reasoning System (NARS), which isasymbolic logic implemented in a computer system, and has many interesting properties that are closely related to intelligence. The de nition also clari es the di erence and relationship between AI and other disciplines, such as computer science. Finally, the de nition is compared with other popular de nitions of intelligence, and its advantages are argued.

irce, 1931). According to Peirce, the deduction /abduction/induction triad is defined formally in terms of the position of the shared term: c fl 1998 Kluwer Academic Publishers. Printed in the Netherlands. wang.tex; 11/06/1998; 21:08; p.1 2 Deduction Abduction Induction M ae P P ae M M ae P S ae M S ae M M ae S ------------ ------------ ------------ S ae P S ae P S ae P Defined in this way, the difference among the three is purely syntactic: in deduction, the shared term is the subject of one premise and the predicate of the other; in abduction, the shared term is the predicate of both premises; in induction, the shared term is the subject of both premises. If we only consider combinations of premises with one shared term, these three exhaust all the possibi

This article analyzes and compares several approaches of formalizing the notion of evidence in the context of general-purpose reasoning system. In each of these approaches, the notion of evidence is defined, and the evidence-based degree of belief is represented by a binary value, a number (such as a probability), or two numbers (such as an interval). The binary approaches provide simple ways to represent conclusive evidence, but cannot properly handle inconclusive evidence. The one-number approaches naturally represent inconclusive evidence as a degree of belief, but lack the information needed to revise this degree. It is argued that for systems opening to new evidence, each belief should at least have two numbers attached to indicate its evidential support. A few such approaches are discussed, including the approach used in NARS, which is designed according to the considerations of general-purpose intelligent systems, and provides novel solutions to several traditional problems on evidence.

Term logic is characterized by subject-- predicate statements and syllogistic inference rules. This kind of logic, when properly extended, provides a natural and consistent model for multiple types of inference, including deduction, abduction, induction and revision. This paper briefly describes how such a logic works in the NARS project. 1 Introduction There are two major traditions in formal logic: term logics and propositional/predicate logics, exemplified respectively by the Syllogism of Aristotle and the FirstOrder Predicate Logic of Frege, Russell, and Whitehead. Term logic is different from predicate logic in both its knowledge representation language and its inference rules. Term logic uses subject--predicate statements, in each of which two terms are linked together by a copula: S ae P where S is the subject term of the statement, and P is the predicate term. Intuitively, this statement says that S is a specialization (instantiation) of P , and P is a generalization (abstra...