Hi, I'm Cédrick Béler, a french PhD student and I've been selected for GSoC of squeak. The selected application consists in porting OpenNARS (Non-Axiomatic Reasoning Systems) to Squeak using Seaside for the GUI. My Mentors are Klaus D.Witzel and Pei Wang (creator/developer of NARS).
NARS is before all a reasoning system but a non-axiomatic one compared to usual semi-axiomatic ones we find in AI [1]. It attempts to uniformly explain and reproduce many cognitive facilities, including reasoning, learning, planning, etc, so as to provide a unified theory, model, and system for AI as a whole. The ultimate goal of NARS research is to build a thinking machine. What makes this system different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. NARS theory exists. A book has been published recently (Rigid flexibility).
Since 2008, NARS is developed as open source software . A prototype exists in java for the core of the system but there is still a lot to do until the aim of having a thinking machine is reached. A port is underway in Python and we propose to start a squeak port because we think a dynamic and flexible environment and language as Smalltalk/Squeak could be very appropriate here.
Right now, I'm finishing writing my PhD dissertation so I will only spend moderate time on the project in June (delivery date is by the end of June). Once done, I'll be nearly full-time on the project. When coding, I'll be as much as possible on IRC (#squeak) on freenode under the nickname Cdrick. Feel free to contact me if you have any question in relation to this project. I will start the port with the stable core of the prototype (see here for more information)
Cédrick Béler (cdrick65 for my google account...)
[1] NARS (Non-Axiomatic Reasoning System) is a concrete example of non-axiomatic system. Here is a short definition of axiomatic, semi-axiomatic and non-axiomatic (quoted from Pei Wang publications)
NARS is before all a reasoning system but a non-axiomatic one compared to usual semi-axiomatic ones we find in AI [1]. It attempts to uniformly explain and reproduce many cognitive facilities, including reasoning, learning, planning, etc, so as to provide a unified theory, model, and system for AI as a whole. The ultimate goal of NARS research is to build a thinking machine. What makes this system different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. NARS theory exists. A book has been published recently (Rigid flexibility).
Since 2008, NARS is developed as open source software . A prototype exists in java for the core of the system but there is still a lot to do until the aim of having a thinking machine is reached. A port is underway in Python and we propose to start a squeak port because we think a dynamic and flexible environment and language as Smalltalk/Squeak could be very appropriate here.
Right now, I'm finishing writing my PhD dissertation so I will only spend moderate time on the project in June (delivery date is by the end of June). Once done, I'll be nearly full-time on the project. When coding, I'll be as much as possible on IRC (#squeak) on freenode under the nickname Cdrick. Feel free to contact me if you have any question in relation to this project. I will start the port with the stable core of the prototype (see here for more information)
Cédrick Béler (cdrick65 for my google account...)
[1] NARS (Non-Axiomatic Reasoning System) is a concrete example of non-axiomatic system. Here is a short definition of axiomatic, semi-axiomatic and non-axiomatic (quoted from Pei Wang publications)
- pure-axiomatic system: In all aspects, the system has sufficient knowledge and resources with respect to the problems to be solved.
- Pure-axiomatic systems are studied in mathematics, and is not directly related to AI
- semi-axiomatic system: In some, but not all, aspects, the system has sufficient knowledge and resources with respect to the problems to be solved.
- Most of the previous AI work in the inference framework belong to the category of semi-axiomatic system, which attempt to make partial extension or revision of mathematical logic, while keep the other parts.
- non-axiomatic system: In all aspects, the system has insufficient knowledge and resources with respect to the problems to be solved.
- For AI, what is really needed are non-axiomatic systems, which do not assume the sufficiency of knowledge and resources in any aspect of the system.
Basically in NARS, solving the same problem several times has not to give the same solution... If you're intrigued by this theory, don't hesitate having a look at the available documentation :)
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