Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence
This work addresses the fundamental issue of computational complexity for AI researchers, though it appears incremental as it builds on existing concepts without demonstrating major breakthroughs.
The paper tackles the challenge of high computational complexity in AI by proposing a framework that integrates trial-and-error with dynamic search, using the Number Partition Problem as an example to illustrate the approach.
Computational complexity is a core theory of computer science, which dictates the degree of difficulty of computation. There are many problems with high complexity that we have to deal, which is especially true for AI. This raises a big question: Is there a better way to deal with these highly complex problems other than bounded by computational complexity? We believe that ideas and methods from intelligence science can be applied to these problems and help us to exceed computational complexity. In this paper, we try to clarify concepts, and we propose definitions such as unparticularized computing, particularized computing, computing agents, and dynamic search. We also propose and discuss a framework, i.e., trial-and-error + dynamic search. Number Partition Problem is a well-known NP-complete problem, and we use this problem as an example to illustrate the ideas discussed.