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From NARS to a Thinking Machine

From NARS to a Thinking Machine. Pei Wang Temple University. Content. NARS (Non-Axiomatic Reasoning System): a project aimed at building a general-purpose intelligent system, or a “thinking machine” The main ideas behind the project The development plan of the project

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From NARS to a Thinking Machine

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  1. From NARS to a Thinking Machine Pei Wang Temple University

  2. Content NARS (Non-Axiomatic Reasoning System): a project aimed at building a general-purpose intelligent system, or a “thinking machine” • The main ideas behind the project • The development plan of the project • The past, present, and future of the project

  3. Observations • “Intelligence” is a capability possessed by human beings, but not by animals and ordinary computers. • The major difference: not in “what it can do”, but in “what it can learn to do”. • Key features: adaptivity, generality, creativity, flexibility, but not absolute optimity.

  4. Methodology • Minimalism: not to maximize the system’s performance, but to minimize its theoretical assumptions and technical instruments, while still achieving desired performance. • There are scientific and engineering reasons for following such an approach. • Many such attempts have failed, but they might have followed wrong ideas.

  5. Basic Principle “Intelligence” is the capability of a system to adapt to its environment and to work with insufficient knowledge and resources. The system should • rely on constant processing capacity, • work in real time, • open to unexpected tasks, • learn from experience.

  6. Framework • NARS is built within the framework of a reasoning system, with a language for knowledge representation, a semantics of the language, a set of inference rules, a memory architecture, a control mechanism. • Advantages: being domain-independent, combining the justifiability of individual steps and the flexibility of processes.

  7. Categorical Language • A typical sentence: bird  animal [1.0, 0.9] • Term: “bird” and “animal” are names of concepts • Inheritance relation “” : special-general • Truth value: [frequency, confidence]

  8. Experience-Grounded Semantics • The truth value of a sentence is determined by available evidence in the experience: f = w+/w, c = w/(w+1) • Truth value uniformly represents randomness, fuzziness, and ignorance. • The meaning of a term is defined by its experienced relations with other terms.

  9. deduction M induction M M P S S P S S P P Basic Inference Rules abduction revision

  10. gull swimmer robin [1.00, 0.90] [1.00, 0.90] [1.00, 0.90] feathered_creature [0.00, 0.90] [1.00, 0.90] [1.00, 0.90] [1.00, 0.90] [1.00, 0.90] crow bird swan Memory as a Belief Network Cbird

  11. Control Strategy • In each step, a task is processed by interacting with a belief, according to certain rules. • The task and belief are selected probabilistically, according to priority distributions among related tasks and beliefs. • Factors influence the priority of an item: quality of the item, usefulness of the item in history, and relevance of the item to the current context.

  12. Compound Terms • Compound terms: sets, intersections, differences, products, and images. • Variants of the inheritance relation: similarity, instance, and property. • New inference rules are added to carry out compound composition and decomposition. • Related changes in memory and control.

  13. Higher-Order Reasoning • Two higher-order relations, implication and equivalence, are defined between statements. • Compound statements: negations, conjunctions, and disjunctions. • The implication relation is used to carry out conditional and hypothetical inferences. • Variable terms are used to carry out general and abstract inferences.

  14. Procedural Reasoning • Events as statements with temporal relations (sequential and parallel). Prediction and explanation as temporal inferences. • Operations as statements with procedural interpretation. Skill learning and planning as procedural inferences. • Goals as statements to be realized. Decision making as the making of new goals.

  15. Development Progress • DONE: language definition semantics specification basic inference rules rules for compound terms rules for higher-order inference basic memory and control • DOING: rules for temporal/procedural inference refined memory and control

  16. NARS Plus Optional extensions of NARS: • sensorimotor interface • natural language interface • education procedure • socialization procedure • special hardware • evolution process

  17. Conclusions • An AI system should follow the same principles as the human mind, though it may have different internal structure, external behavior, practical ability, etc. • To see intelligence as “adaptation with insufficiency” explains mental processes, guides system design, and distinguishes AI from other disciplines.

  18. Information about NARS • Website: containing 30+ publications and on-line demonstrations (a Java Applet and a Prolog program) of NARS (Version 4.2). (http://www.cogsci.indiana.edu/farg/peiwang/papers.html) • Book: Rigid Flexibility: The Logic of Intelligence, Springer, ISBN 1402050445, Available: September 15, 2006. (Wang-Contents-Preface.pdf)

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