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,

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.

Abstract. Non-Axiomatic Logic (NAL) is designed for intelligent reasoning, and can be used in a system that has insufficient knowledge and resources with respect to the problems to be solved. This paper reports the result of a case study that applies NAL in medical diagnostics, and the logic is compared with binary logic and probability theory.

The concept of computation, as well as the related concepts algorithm, Turing machine, computability, and computational complexity, correspond to a specific mode of using computers to solve problems. This computational mode assumes the sufficiency of knowledge and resources with respect to the problem to be solved. From the view point of Artificial Intelligence, an intelligent mode of problem solving is introduced, where the problem solving process cannot been seen as computation anymore. A system working in this mode is briefly described. Finally, these two modes of problem solving are compared. “Computation ” is a fundamental concept in computer science. Artificial Intelligence (AI) is usually taken as a branch of computer science, and it has also, to a large extent, inherited the theoretical heritage associated to the concept of computation. In this paper we argue that it is improper to treat intelligent problem solving as computation, though this conclusion