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Dimensions of complexity of intelligent agents

This paper aims to provide a framework for understanding the construction of intelligent agents. This is used to explain the history of AI, and provide a roadmap of future research. Research has progressed by making simplifying assumptions about the representations of the agents or about the environments the agents act in. In particular, we present a number of dimensions of simplifying assumptions that have been made. For each of these dimensions, there is a simplified case and progressively more complex cases. We argue that an intelligent agent needs the complex value in each of these dimensions (i.e., to simultaneously give up many simplifying assumptions). However these dimensions interact in complex ways. Much of the recent history can be seen as understanding the interaction of these dimensions.