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- W4315589020 abstract "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." @default.
- W4315589020 created "2023-01-11" @default.
- W4315589020 creator A5066528840 @default.
- W4315589020 date "2022-12-22" @default.
- W4315589020 modified "2023-09-26" @default.
- W4315589020 title "Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence" @default.
- W4315589020 doi "https://doi.org/10.48550/arxiv.2301.03384" @default.
- W4315589020 hasPublicationYear "2022" @default.
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