arXiv:2510.02655v2 Announce Type: replace Abstract: This paper offers a new concept of {\it possibility} as an alternative to the now-a-days standard concept originally introduced by L.A. Zadeh in 1978. This new version was inspired by the original but, formally, has nothing in common with it other than that they both adopt the {\L}ukasiewicz multivalent interpretation of the logical connectives. Moreover, rather than seeking to provide a general notion of possibility, this focuses specifically on the possibility of a real-world event. An event is viewed as having prerequisites that enable its
This paper offers a new approach to possibility in AI, diverging from Zadeh's 1978 concept, suggesting a current drive to refine foundational AI theories beyond existing paradigms.
A revised mathematical framework for 'possibility' could lead to more nuanced and robust AI systems, enhancing their ability to reason about real-world uncertainties and events.
The conceptual model for how AI interprets and integrates real-world event possibilities is undergoing a fundamental re-evaluation, moving beyond traditional fuzzy logic interpretations.
- · AI researchers
- · Autonomous system developers
- · Risk assessment platforms
- · AI systems overly reliant on Zadeh's fuzzy possibility theory
AI models may be able to better anticipate and react to unforeseen real-world conditions by using this refined possibility concept.
Improved possibility models could enhance the reliability and safety of AI in critical applications like self-driving cars or medical diagnostics.
More sophisticated philosophical foundations for AI reasoning might emerge, fostering a deeper understanding of artificial intelligence's potential and limitations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI