【通用人工智能】基于java开发的人工智能推理系统

<div style="text-align:center"><img src="https://user-images.githubusercontent.com/8284677/74609985-02087e80-50e7-11ea-9562-218dec34714d.png" height="250"></div> Implementation of a Non-Axiomatic Reasoning System [6], a general-purpose reasoner that adapts under the Assumption of Insufficient Knowledge and Resources [7]. This is a completely new platform and not branched from the existing OpenNARS codebase. The ONA (OpenNARS for Applications) system [1] takes the logic and conceptual ideas of OpenNARS, the event handling and procedure learning capabilities of ANSNA [2, 3], and the control model from ALANN [4]. The system is written in C, is more capable than our previous implementations, and has also been experimentally compared with Reinforcement Learning [5]. The ONA implementation has been developed with a pragmatic mindset. The focus on the design has been to implement the 'existing' theory [6, 7] as effectively as possible and make firm decisions rather than keep as many options open as possible. This has led to some small conceptual differences to OpenNARS [8] which was developed for research purposes. Video tutorials and demo videos can be found here: [Video tutorials](https://github.com/opennars/OpenNARS-for-Applications/wiki/Video-tutorials) ***How to clone and compile (tested with GCC and Clang for x64, x86 and ARM):*** ``` git clone https://github.com/opennars/OpenNARS-for-Applications cd OpenNARS-for-Applications ./build.sh ``` ***How to set the amount of threads the system should run with: (to be tested more, compile with ./build.sh -fopenmp)*** ``` export OMP_NUM_THREADS=4 // 4 threads seems to be the sweet spot. More threads leads to more contention and less speed currently ``` ***How to run the interactive Narsese shell:*** ``` ./NAR shell ``` ***with syntax highlighting:*** ``` ./NAR shell | python3 colorize.py ``` ***with English NLP shell and syntax highlighting:*** ``` python3 english_to_narsese.py | ./NAR shell | python3 colorize.py ``` ***How to run the C tests and then receive instructions how to run the current example programs:*** ``` ./NAR ``` ***How to run all C tests, and all Narsese and English examples as integration tests, and collect metrics across all examples:*** ``` python3 evaluation.py ``` For the current output, see [Evaluation results](https://github.com/opennars/OpenNARS-for-Applications/wiki/Evaluation-Results-(Tests,-metrics)) **How to run an example file:** Narsese: ``` ./NAR shell < ./examples/nal/example1.nal ``` English: (tested with NLTK v3.4.5, v3.5) ``` python3 english_to_narsese.py < ./examples/english/story1.english | ./NAR shell ``` **How to run an UDPNAR:** ``` ./NAR UDPNAR IP PORT timestep(ns per cycle) printDerivations ./NAR UDPNAR 127.0.0.1 50000 10000000 true ``` where the output can be logged simply by appending ``` > output.log ``` **How to reach us:** Real-time team chat: #nars IRC channel @ freenode.net, #nars:matrix.org (accessible via Riot.im) Google discussion group: https://groups.google.com/forum/#!forum/open-nars **References** [1] Hammer, P., & Lofthouse, T. (2020, September). ‘OpenNARS for Applications’: Architecture and Control. In International Conference on Artificial General Intelligence (pp. 193-204). Springer, Cham. [2] Hammer, P. (2019, August). Adaptive Neuro-Symbolic Network Agent. In International Conference on Artificial General Intelligence (pp. 80-90). Springer, Cham. [3] Hammer, P., & Lofthouse, T. (2018, August). Goal-directed procedure learning. In International Conference on Artificial General Intelligence (pp. 77-86). Springer, Cham. [4] Lofthouse, T. (2019). ALANN: An event driven control mechanism for a non-axiomatic reasoning system (NARS). [5] Eberding, L. M., Thórisson, K. R., Sheikhlar, A., & Andrason, S. P. (2020). SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners. In Artificial General Intelligence: 13th International Conference, AGI 2020, St. Petersburg, Russia, September 16–19, 2020, Proceedings (Vol. 12177, p. 72). Springer Nature. [6] Wang, P. (2013). Non-axiomatic logic: A model of intelligent reasoning. World Scientific. [7] Wang, P. (2009, October). Insufficient Knowledge and Resources-A Biological Constraint and Its Functional Implications. In AAAI Fall Symposium: Biologically Inspired Cognitive Architectures. [8] Hammer, P., Lofthouse, T., & Wang, P. (2016, July). The OpenNARS implementation of the non-axiomatic reasoning system. In International conference on artificial general intelligence (pp. 160-170). Springer, Cham.