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Computational complexity is a core theory of computer science, which dictatesthe degree of difficulty of computation. There are many problems with highcomplexity that we have to deal, which is especially true for AI. This raises abig question: Is there a better way to deal with these highly complex problemsother than bounded by computational complexity? We believe that ideas andmethods from intelligence science can be applied to these problems and help usto exceed computational complexity. In this paper, we try to clarify concepts,and we propose definitions such as unparticularized computing, particularizedcomputing, computing agents, and dynamic search. We also propose and discuss aframework, i.e., trial-and-error + dynamic search. Number Partition Problem isa well-known NP-complete problem, and we use this problem as an example toillustrate the ideas discussed.

Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence