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.

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.

The existence of an optimal control policy and the techniques for finding it are grounded fundamentally in a superlative perspective. These techniques can be of limited value when the global behavior of the system is difficult to characterize, as it may be when the system is nonlinear, when the input is constrained, or when only partial information is available regarding system dynamics or the environment. Satisficing control theory is an alternative approach that is compatible with such systems. This theory is extended by the introduction of the notion of strongly satisficing to provide a rigorous, systematic procedure for the design of satisficing controllers which are consistent with optimal control theory. Because they are often difficult to solve optimally, one application of satisficing control theory is to nonlinear control problems. Of particular interest are the nonlinear quadratic regulator and nonlinear minimum time problems. A controller synthesis procedure and resulting so...

we will try to consider a perceptive analysis of the optimal stopping problem. In this paper, the fuzzy perception value of the expectation of the optimal stopped reward is characterized and calculated by a new recursive equation. Also, a numerical example described by triangular fuzzy numbers is given.

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...

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”.

It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the IS problem domain, may be difficult to model in crisp or precise terms.

"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...