Tour Overview - wpower12/opennarswikiv2 GitHub Wiki

A high-level overview of the OpenNARS implementation

Open-NARS is the open-source version of NARS (Non-Axiomatic Reasoning System), a general-purpose AI system, designed in the framework of a reasoning system.

NARS works by processing tasks imposed by human users, other computer systems, as well as its own needs. Tasks can arrive at any time, and there is no restriction on their contents, as far as they can be expressed in Narsese, the I/O language of NARS (see Input Output Format for the Grammar of Narsese).

There are several types of tasks:

  • Judgment. To process it means to accept it as the system's belief, as well as to derive new beliefs and to revise old beliefs accordingly.

  • Goal. To process it means to carry out some system operations to realize it. (Not available in versions 1.5.n.)

  • Question. To process it means to find the best answer to it according to current beliefs and desires.

As a reasoning system, the architecture of NARS consists of a memory, an inference engine, and a control mechanism.

nars box diagram

The memory contains a collection of concepts, a list of operators, and a buffer for new tasks. Each concept is identified by a term, and contains tasks and beliefs directly on the term, as well as links to related tasks and terms.

nars memory

The inference engine carries out various type of inference, according to a set of built-in rules. Each inference rule derives certain new tasks from a given task and a belief that are related to the same concept.

Roughly speaking, the control mechanism repeatedly carries out the working cycle of the system, consisting of the following steps:

nars reasoning cycle

All the selections in steps 1 and 2 are probabilistic, in the sense that all the items (tasks, beliefs, or concepts) within the scope of the selection have priority values attached, and the probability for each of them to be selected at the current moment is proportional to its priority value. When an new item is produced, its priority value is determined according to its parent items, as well as the type of mechanism that produces it. At step 6, the priority values of all the involved items are adjusted, according to the immediate feedback of the current cycle.

At the current time, the most comprehensive description of NARS are the books Rigid Flexibility: The Logic of Intelligence (2006) and Non-Axiomatic Logic: A Model of Intelligent Reasoning (2013). Various aspects of the system are introduced and discussed in many papers, most of which are available here.

Beginners may be interested in the following documents. First, a collection of 'Tours' describing core components of the system and their implementation.

In addition, the following online materials cover the theory behind NARS, as well as some more in depth descriptions of the control logic.

To test the current implementation, download the most recent version, then try the working examples in the ZIP file. These examples are explained in Single Step Testing Cases and Multi-Step Examples.

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