Simulating Intelligent Behavior Requires a Complex Approach

Troy D. Kelley

This paper will propose three arguments for a multilevel heterogeneous approach to Artificial Intelligence (AI) hard problems. By a heterogeneous approach, we mean the use of multiple methodologies (symbolic, sub-symbolic, subsumption) to solve AI problems. First, if one accepts the postulate that cognitive psychological principles can be beneficial to AI, then one must look at the heterogeneous nature of the human cognitive system. The brain is not homogeneous; it is a collection of different cellular organizations performing different functions. Secondly, there are several examples from the cognitive systems literature that show hybrid approaches provide effective solutions to complex problems. In some cases, these approaches have been better than a single approach. Finally, cognition is so complex, so full of subtle nuance and interwoven interdependencies, that a multiple level heterogeneous approach is the only approach that will prove to be successful in the long term. In other words, the complexity of perceiving and understanding the environment in a human manner necessitates a multilevel approach.


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