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.

With conicting evidence, a reasoning system derives uncertain conclusions. If the system is open to new evidence, it faces additionally a higher-order uncertainty, because the rst-order uncertainty evaluations are uncertain themselves | they can be changed by future evidence. A new measurement, condence, is introduced for this higher-order uncertainty. It is de- ned in terms of the amount of available evidence, and interpreted and processed as the relative stability of the rst-order uncertainty evaluation. Its relation with other approaches of \reasoning with uncertainty " is also discussed. Keywords. condence, evidence, frequency interval, revision, inference, deduction, induction, abduction. 1

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.

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”.

Abstract. Sequence prediction is a key component of intelligence. This can be

This paper shows that a constraint on universal Turing machines is necessary for Legg's and Hutter's formal measure of intelligence to be unbiased. Their measure, defined in terms of Turing machines, is adapted to finite state machines. A No Free Lunch result is proved for the finite version of the measure.

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 paper proposes a new interpretation of Wason's selection task. According to it, the results of the experiment do not show that human reasoning is not logical, but that the traditional logic is not a proper normative theory of reasoning under certain conditions. A new logic is introduced, which is consistent with the experiment results of the task. THE SELECTION TASK In Wason's selection task, subjects see four cards showing symbols like E, K, 4, and 7, and are told that each card has a letter on one side and a number on the other. The task is to choose the cards that need to be turned over in order to determine whether the following rule is true or false: If a card has a vowel on one side, then it has an even number on the other side. Most subjects choose E alone, or E and 4, while the correct answer is E and 7, because Any o

The Novamente AI Engine is briefly reviewed. The overall architecture is unique, drawing on system-theoretic ideas regarding complex mental dynamics and associated emergent patterns. We describe how these are facilitated by a novel knowledge representation which allows diverse cognitive processes to interact effectively. We then elaborate the two primary cognitive algorithms used to construct these processes: probabilistic term logic (PTL), and the Bayesian Optimization Algorithm (BOA). PTL is a highly flexible inference framework, applicable to domains involving uncertain, dynamic data, and autonomous agents in complex environments. BOA is a population-based optimization algorithm which can incorporate prior knowledge. While originally designed to operate on bit strings, our extended version also learns programs and predicates with variable length and tree-like structure, used to represent actions, perceptions, and internal state. We detail some of the specific dynamics and structures we expect to emerge through the interaction of the cognitive processes, outline our approach to training the system through experiential interactive learning, and conclude with a description of some recent results obtained with our partial implementation, including practical work in bioinformatics, natural language processing, and knowledge discovery.

First-order predicate logic meets many problems when used to explain or reproduce cognition and intelligence. These problems have a common nature, that is, they all exist outside mathematics, the domain for which mathematical logic was designed. Cognitive logic and mathematical logic are fundamentally different, and the former cannot be obtained by partially revising or extending the latter. A reasoning system using a cognitive logic is briefly introduced, which provides solutions to many problems in a unified manner. 1 Mathematical logic and cognition An automatic reasoning system usually consists of the following major components: 1. a formal language that represents knowledge, 2. a semantics that defines meaning and truth value in the language, 3. a set of inference rules that derives new knowledge, 4. a memory that stores knowledge, 5. a control mechanism that chooses premises and rules in each step. The first three components are usually referred to as a logic, or the logical part of the reasoning system, and the last two as an implementation of the logic, or the control part of the system. At the present time, the most influential theory for the logic part of reasoning systems is mathematical logic, especially, first-order predicate logic. For the control part, it is the theory of computability and computational complexity. Though these theories have been very successful in many domains, their application in cognitive science and artificial intelligence shows fundamental differences from human reasoning in similar situations.