a fly-ball can be calculated by variables such as the running speed of the outfielder and the arc of the baseball. However, Gibson's work shows that a simpler method is possible. The outfielder can catch the ball so long as they adjust their running speed so that the ball continually moves in a straight line in their field of vision. Note that this strategy uses various affordances that are contingent upon the success of the outfielder, including their physical body composition, the environment of the baseball field, and the sensory information obtained by the outfielder. Clark points out here that the latter strategy of catching the ball as opposed to the former has significant implications for perception. The affordance approach proves to be non-linear because it relies upon spontaneous real-time adjustments. On the contrary, the former method of computing the arc of the ball is linear as it follows a sequence of perception, calculation and performing action. Thus, the affordance approach challenges the traditional view of perception by arguing against the notion that computation and introspection are necessary. Instead, it ought to be replaced with the idea that perception constitutes a continuous equilibrium of action adjustment between the agent and the world. Ultimately Clark does not expressly claim this is certain but he does observe the affordance approach can explain adaptive response satisfactorily. This is because they utilize environmental cues made possible by perceptual information that is actively used in the real-time by the agent. General principles of intelligent behavior In the formation of general principles of intelligent behavior, Pfeifer intended to be contrary to older principles given in traditional artificial intelligence. The most dramatic difference is that the principles are applicable only to situated robotic agents in the real world, a domain where traditional artificial intelligence showed the least promise. Principle of cheap design and redundancy: Pfeifer realized that implicit assumptions made by engineers often substantially influence a control architecture's complexity. This insight is reflected in discussions of the scalability problem in robotics. The internal processing needed for some bad architectures can grow out of proportion to new tasks needed of an agent. The proposed solutions are to have the agent exploit the inherent physics of its environment, to exploit the constraints of its niche, and to have agent morphology based on parsimony and the principle of Redundancy. Redundancy reflects the desire for the error-correction of signals afforded by