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Psychological evidence shows that probability theoryisnotaproper descriptive model of intuitive human judgment. Instead, 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 designed under the assumption that the system's knowledge and resources are insu cient with respect to the questions that the system needs to answer. The proposed heuristics in human reasoning can also be observed inthis new model, and can be justi ed according to the assumption.

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”.

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...