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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The aim of this paper is to introduce the theoretical foundations of an approach for intelligent systems development. Derived from semiotics, a classic discipline in human sciences, the theory developed provides a mathematical framework for the concept of knowledge and for knowledge processing. As a result, a new perspective to study and to develop intelligent systems emerges. A taxonomy of elementary types of knowledge is proposed based on the classification of types of signs in semiotics, followed by a another classification of knowledge from the point of view of application in cognitive systems. In addition, we propose the mathematical definition of objects, objects systems and objects networks, to model mathematically the different types of knowledge described. The symbiosis of such key concepts introduces a computational paradigm to develop and implement intelligent systems, called here computational semiotics.

I present D cognitive model of the humon ability lo acquire c.us.I relotionshipr. I report on experimental evidence demonrtroting that human leornerr acquire occwote cwxd relationships more rapidly when training examples oreconrirtent with o general theory of cwsolity. This article describes o learning procerr that uses o general theory of causality OS background knowledge. The leorning pro-cess, which I cdl theory-driven learning (TDL), hypothesizes cw~a1 relationships consistent both with observed doto and the general theory of courolity. TDL accounts for data on both the rote a+ which humon learners acquire couscll relo-tionrhips, and the types of COUSJ relationships they acquire. Experiments with TDL demonrtrote the odvontoge of TDL for acquiring cowa relationships over similarity-bored opproacher to learning: Fewer examples ore required to loom an acc~rote relotionrhio. 1.

Non-Axiomatic Reasoning System (NARS) is an intelligent reasoning system, where intelligence means working and adapting with insu cient knowledge and resources. NARS uses a new form of term logic, or an extended syllogism, in which several types of uncertainties can be represented and processed, and in which deduction, induction, abduction, and revision are carried out in a uni ed format. The system works in an asynchronously parallel way. The memory of the system is dynamically organized, and can also be interpreted as a network. After present the major components of the system, its implementation is brie y described. An example is used to show howthe system works. The limitations of the system are also discussed. 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.

Abduction is an inference mechanism where given a knowledge base and some observations, the reasoner tries to find hypotheses which together with the knowledge base explain the observations. A reasoning based on such an inference mechanism is referred to as abductive reasoning. Given a theory and some observations, by filtering the theory with the observations, we mean selecting only those models of the theory that entail the observations. Entailment with respect to these selected models is referred to as filter entailment. In this paper we give necessary and sufficient conditions when abductive reasoning with respect to a theory and some observations is equivalent to the corresponding filter entailment. We then give sufficiency conditions for particular knowledge representation formalisms that guarantee that abductive reasoning can indeed be done through filtering and present examples from the knowledge representation literature where abductive reasoning is done through filtering. We...

The purpose of this paper is to discuss methods for evaluating the intelligence of intelligent systems by means of Computational Semiotics concepts. Instead of looking at the system as a black box and testing its behavior, the process described in this paper focus on architectural details of structures, organizations, processes and algorithms used in the construction of the intelligent system, evaluating the impact of using these elements in the overall intelligent behavior exhibited by the system. It proposes then an "insider" type of metrics that, coupled to "outsider" metrics, we hope will be important for the determination of general metrics of intelligence in intelligent systems.

this dissertation. More precisely, we are referring to what is called the orthodox version of these logics in these works

Classical logic is the study of ”safe ” formal reasoning. Western Philosophers de-veloped classical logic over a period of thirty-three centuries after its introduction in the form of syllogistic by Aristotle [1] in the third century B. C. Beginning in the nineteenth century with De Morgan [2] and Boole [3], responsibility for the develop-ment of classical logic moved from the philosophical to the mathematical community.