Open-NARS is the open-source version of NARS, a general-purpose AI system, designed in the framework of a reasoning system. This project is an evolution of the v1.5 system. The mailing list discusses both its theory and implementation.
Using mvn:
for each of the projects:
https://github.com/opennars/opennars-parent.git
https://github.com/opennars/opennars.git
https://github.com/opennars/opennars-lab.git
https://github.com/opennars/opennars-applications.git
https://github.com/opennars/opennars-gui.git
git clone 'project'
cd 'project_dir'
mvn clean install
optionally append -Dmaven.javadoc.skip=true to skip documentation generation
cd 'project_dir'
mvn exec:java
Alternatively, using IntelliJ IDE:
Install git https://git-scm.com/downloads
Install OpenJDK 11 https://jdk.java.net/11/
Install community edition IntelliJ https://www.jetbrains.com/idea/download/
Checkout https://github.com/opennars/opennars.git
Checkout https://github.com/opennars/opennars-lab.git
Checkout https://github.com/opennars/opennars-applications.git
You can either checkout within Intellij or use the Github desktop (available from the github clone button in the repo)
Build opennars
If this is a fresh install you will be prompted to enter the jdk path (where you installed it above) You may be prompted to update maven dependencies - do this if prompted
Build opennars-lab
Select org.opennars.lab.launcher.Launcher as the main entry point
Build opennars-applications
Select org.opennars.applications.Launcher
The launchers are the easiest way to run the various apps
opennars-lab
Main GUI - Main user interface for NARS
Test Chamber - Simulation environment for testing behaviours
Micro world - Behaviour learning by simple insect like creature
NAR Pong - The classic pong game
Language Lab - For experimenting with parts of speech (POS) and grammar learning
Perception Test - Pattern matching experiment
Prediction Test - Predicts a waveform - Can be run directly from Intellij (Current issue with running with launcher)
Vision - Vision experiment - Can be run direcly from Intellij (Current issue with running with launcher)
opennars-applications
Main GUI - A simple MIT license GUI
Crossing - A smart city traffic intersection simulation
Identity mapping - An experimental setup for testing aspects of Relations Frame Theory (RFT)
Opennars Core is run directly by the Lab and Applications Launchers.
Here is a link to some Narses examples including:
Toothbrush example - how to use a toothbrush to undo a screw?
Detective example - who is the criminal?
https://github.com/opennars/opennars/tree/master/src/main/resources/nal/application
Non-Axiomatic Reasoning System (NARS) processes tasks imposed by its environment, which may include human users or other computer systems. 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.
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.
- Question - To process it means to find the best answer to it according to current beliefs.
- Goal - To process it means to carry out some system operations to realize it.
As a reasoning system, the architecture of NARS consists of a memory, an inference engine, and a control mechanism.
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.
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.
The control mechanism repeatedly carries out the working cycle of the system, generally consisting of the following steps:
- Select tasks in the buffer to insert into the corresponding concepts, which may include the creation of new concepts and beliefs, as well as direct processing on the tasks.
- Select a concept from the memory, then select a task and a belief from the concept.
- Feed the task and the belief to the inference engine to produce derived tasks.
- Add the derived tasks into the task buffer, and send report to the environment if a task provides a best-so-far answer to an input question, or indicates the realization of an input goal.
- Return the processed belief, task, and concept back to memory with feedback.
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 5, 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 and Non-Axiomatic Logic: A Model of Intelligent Reasoning . Various aspects of the system are introduced and discussed in many papers, most of which are available here.
Beginners can start at the following online materials:
- The basic ideas behind the project: The Logic of Intelligence
- The high-level engineering plan: From NARS to a Thinking Machine
- The core logic: From Inheritance Relation to Non-Axiomatic Logic
- The semantics: Experience-Grounded Semantics: A theory for intelligent systems
- The memory and control: Computation and Intelligence in Problem Solving
The core is derived from the code of Pei Wang.
- Java 8+ (OpenJDK 10 recommended)
For an overview of reasoning features, see working examples (tests) in the nal folder, also explained in SingleStepTestingCases and MultiStepExamples.
- Maven
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent