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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From laser-scanned data to feature human model: a system based on

Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems. 1. Introduction Induction and deduction are both underlying processes in intelligent agents. Induction "involves intellectual leaps from the particular to the general" (D'Ignazio & Wold, 1984). It plays an important part in knowledge acquisition or learning. D'Ignazio and Wold (1984) claim that in...

By analyzing the relationships among chance, weight of evidence and degree ofbelief, it is shown that the assertion \chances 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, some fundamental postulates of the theory must be rejected. A new approach for uncertainty management is introduced, which shares many intuitive ideas with D-S theory, while avoiding this problem. 1

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.

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 con icting beliefs. The current solution accepted in the two domains is the speci city 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. 1

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. 1 Introduction Semantics studies how the items in a language are related to the environment in which the language is used. Concretely, semantics is the theory of meaning and truth. To ask questions like "What is the meaning of a term?" and "What is the truth value of a sentence?", what we are looking for are the principles that determining meaning and truth in general, rather than the meaning of a specific wo...

"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 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.