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Behavior Robot
Introduction
As a design strategy, the behavior-based approach has produced intelligent systems for ciple underlying all biological intelligence. (Brooks 1990) To many, this theoretical question simply was not the issue. Instead of focusing on designing systems that could think intelligently, the emphasis had changed to creating agents that could
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Junior: An all-terrain robot recently used to deploy a gamma-locating device within a radioactive environment.
 
act intelligently. From an engineering point of view, this change rejuvenated robotic design, producing physical robots that could acplish real-world tasks without being told e*actly how to do them. From a scientific point of view, researchers could now avoid high-level, armchair discussions about intelligence. Instead, intelligence could be assessed more objectively as a measurement of rational behavior on some task. Since successful pletion of a task was now the goal, researchers no longer focused on designing elaborate processing systems and instead tried to make the coupling between perception and action as direct as possible. This aim remains the distinguishing characteristic of behavior-based robotics.
The sub-sections which follow e*plain the roots of behavior based robotics, how it rose as a counter to the symbolic, deliberative approach of classical AI and how it has e to be a standard approach for developing autonomous robots.
A special thanks to Ronald Arkin whose book, Behavior Based Robotics, has greatly influenced this report.
Understanding the Conte*t of Classical AI
Classical AI spent decades trying to model human-like intelligence, using knowledge-based systems that processed representation at a high, symbolic level. Symbolic representation was considered of paramount importance because it allowed agents to operate on sophisticated human concepts and report on their action at a linguistic level. As Donald Michie stated, "In AI-type learning, e*plainability is al