Abstract
AGI systems should be able to pursue their many goals autonomously while operating in realistic environments which are complex, dynamic, and often novel. This paper discusses the theory and mechanisms for goal generation and management in Non-Axiomatic Reasoning System (NARS). NARS works to accomplish its goals by performing executable actions while integrating feedback from its experience to build subjective, but useful, predictive and meaningful models. The system’s ever-changing knowledge allows it to adaptively derive new goals from its existing goals. Derived goals not only serve to accomplish their parent goals but also represent independent motivation. The system determines how and when to pursue its many goals based on priority, context, and knowledge acquired from its experience and reasoning capabilities.
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Notes
- 1.
A human (or other intelligent system) will not necessarily be able to achieve any arbitrary goal, especially with limited knowledge and resources.
- 2.
This potential temporary nature of goals may be overcome by periodically re-inputting the goals into NARS.
- 3.
To represent events that are desired to occur in the future, we can simply add a temporal condition to the goal.
- 4.
Equivalently, the event \(\lnot S\)’s occurrence.
- 5.
Expectation is an estimate of future frequency.
- 6.
Although currently this threshold is a constant, in future implementations it can be treated as a context-dependent variable.
- 7.
Though a limited record is kept regarding its origins.
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Hahm, C., Xu, B., Wang, P. (2022). Goal Generation and Management in NARS. In: Goertzel, B., Iklé, M., Potapov, A. (eds) Artificial General Intelligence. AGI 2021. Lecture Notes in Computer Science(), vol 13154. Springer, Cham. https://doi.org/10.1007/978-3-030-93758-4_11
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