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 20NAR1 [11], and the control model from ALANN [4]. The system is written in C, is more capable than our previous implementations in terms of reasoning performance, and has also been experimentally compared with Reinforcement Learning [5, 6] and means-end reasoning approaches such as BDI models [6]. Additionally, it has become the core reasoning component of a system assisting first responders (Trusted and explainable Artificial Intelligence for Saving Lives, [6]) while driving and completing their mission. This was done in cooperation with NASA Jet Propulsion Laboratory. Also it has been tried for real-time traffic surveillance in cooperation with Cisco Systems [7]. Last, initial experiments for using the system for autonomous robots have been carried out [6], and more is yet to come.
The ONA implementation has been developed with a pragmatic mindset. The focus on the design has been to implement the 'existing' theory [8, 9] 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 [10] which was developed for research purposes.
Video tutorials and demo videos can be found here: Video tutorials Or click on the picture to watch the newest summary videos (summary and demo):
Procedure learning demos (variants of Pong and Space Invaders, Test Chamber, Cartpole, food collecting agent, ...): https://www.youtube.com/watch?v=oyQ250H5owE
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
Additionally the parameter -DHARDENED can be passed to build.sh to end up with a slimmer system without language learning abilities.
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
If you have trouble building with OpenMP, then you probably need to specify library (and / or sources) directory alongside the -fopenmp option, like -L<path to your openmp> or -I<path to your openmp>.
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
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 @ libera.chat, #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). NARS2019 workshop at AGI 2019.
[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] Hammer, P. (2021, July). Autonomy through real-time learning and OpenNARS for Applications. PhD thesis at Department of Computer and Information Sciences, Temple Universitiy
[7] Hammer, P., Lofthouse, T., Fenoglio, E., Latapie, H., & Wang, P. (2020, September). A reasoning based model for anomaly detection in the Smart City domain. In Proceedings of SAI Intelligent Systems Conference (pp. 144-159). Springer, Cham.
[8] Wang, P. (2013). Non-axiomatic logic: A model of intelligent reasoning. World Scientific.
[9] Wang, P. (2009, October). Insufficient Knowledge and Resources-A Biological Constraint and Its Functional Implications. In AAAI Fall Symposium: Biologically Inspired Cognitive Architectures.
[10] 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.
[11] Wünsche, R. (2021, October). 20NAR1-An Alternative NARS Implementation Design. In International Conference on Artificial General Intelligence (pp. 283-291). Springer, Cham.