  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.
