This paper focuses on the relation of heuristic search and level of intelligence in cognitive computation systems. The paper begins with a review of the fundamental properties of a cognitive computation system, which is defined generally as a control system that generates goal-directed actions in response to environmental inputs and constraints. An important property of cognitive computations is the need to process local cues in symbol structures to access and integrate distal knowledge to generate a response. To deal with uncertainties involved in this local-to-distal processing, the system needs to perform heuristic search to locate and integrate the right set of distal structures. The level of intelligence of the system depends critically on the efficiency of the heuristic search process. It is argued that, for a bounded rationality system, the level of intelligence does not depend on how much search it needs to do to accomplish a task. Rather, the level of intelligence depends on how much search it does not need to do to achieve the same level of performance. Examples were discussed to illustrate this idea. The first two examples show how machines that play games like tic-tac-toe and chess rely heavily on the efficiency of the heuristic search algorithm to achieve better performance, demonstrating the relation of heuristic search and intelligence in a bounded rationality system. The second example shows how humans adapt to different information ecologies to perform information search on the Internet and how their performance improves over time, demonstrating how heuristic search can be improved in an adaptive rationality system. The two examples demonstrate how better search control knowledge and representations of task environment can improve the efficiency of heuristic search, thereby improving the intelligence of the system.
