Insufficient Knowledge and Resources — A Biological Constraint and Its Functional Implications
Pei Wang · 2009
Insufficient knowledge and resources is not only a biological constraint on human and animal intelligence, but also has important functional implications for artificial intelligence (AI) systems. Traditional theories dominating AI research typically assume some kind of sufficiency of knowledge and resources, so cannot solve many problems in the field. AI needs new theories obeying this constraint, which cannot be obtained by minor revisions or extensions of the traditional theories. The practice of NARS, an AI project, shows that such new theories are feasible and promising in providing a new theoretical foundation for AI. AI and Biological Constraints Since its inception, Artificial Intelligence (AI) has been driven by two contrary intuitions, both hinted in the name of the field. On one hand, the only form of “intelligence ” we know for sure comes from biological systems. Therefore, it seems natural for AI research to be biologically inspired, and for AI systems to be developed under biological constraints. After all, there is existing proof that this approach works. Though many researchers prefer the more abstract languages provided by neuroscience or psychology over the language of biology, their work can still be considered as “biologically inspired”, in a broad sense. To some researchers, “This is what the brain does ” is a good justification for a design decision in an AI system (Reeke and Edelman 1988). On the other hand, the “artificial ” in the name refers to computers, which are not biological in hardware, and so do not necessarily follow biological principles. Therefore, it also seems natural for AI to be considered as a branch of computer science, and for the systems to be designed under computational considerations (which usually ignore biological constraints). After all, the biological form of intelligence should only be one of the possible forms, otherwise AI is doomed to fail. Some researchers are afraid that to follow biology (or cognitive science) too closely may unnecessarily limit our imagination on how an intelligent system can be built, and the “biological way ” is often not the best way for computers to solve problems (Korf 2006). Copyright c ○ 2009, Association for the Advancement of Artificial