Non-Axiomatic Reasoning System is an adaptive system that works with insu cient knowledge and resources. At the beginning of the paper, three binary term logics are de ned. The rst 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 de ned. 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.

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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 isasymbolic logic implemented in a computer system, and has many interesting properties that are closely related to intelligence. The definition also clari es 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.

With conicting evidence, a reasoning system derives uncertain conclusions. If the system is open to new evidence, it faces additionally a higher-order uncertainty, because the rst-order uncertainty evaluations are uncertain themselves | they can be changed by future evidence. A new measurement, condence, is introduced for this higher-order uncertainty. It is de- ned in terms of the amount of available evidence, and interpreted and processed as the relative stability of the rst-order uncertainty evaluation. Its relation with other approaches of \reasoning with uncertainty " is also discussed. Keywords. condence, evidence, frequency interval, revision, inference, deduction, induction, abduction. 1

An experience-grounded semantics is introduced for an intelligent reasoning system, which is adaptive, and works with insufficient knowledge and resources. According to this semantics, truth and meaning are defined with respect to the experience of the system — the truth value of a statement indicates the amount of available evidence, and the meaning of a term indicates its experienced relations with other terms. The major difference between experience-grounded semantics and modeltheoretic semantics is that the former does not assume the sufficiency of knowledge and resources. This approach provides new ideas to the solution of some important problems in cognitive science.

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...

This paper introduces a single-camera-based stereo vision system used for creating local 3D occupancy models. The design of the system is described, the range data error analysis is presented and the sensor model which assigns the values of evidence to the registered data is built. The application of the proposed system for mobile robot world exploration is shown. Data obtained by running a single camera mobile robot are presented. Keywords: Occupancy grids, visual sensor model, range data fusion, evidence theory. 1 Introduction In world exploration, the occupancy model of the world is one of the most commonly used [3, 20, 7, 25]. In this model, the evidence that a point in space is occupied is calculated, based on the data registered by a range sensor. Originally developed for building 2D maps [9], the occupancy-based approach has recently been extended to build 3D models of the world [15, 17], which provide much more information about the environment. There are two problems howev...

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.

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.