• How can artifacts operate under their own control?
Ktesibios of Alexandria (c. 250 B.C.) built the first self-controlling machine: a water clock with a regulator that maintained a constant flow rate. This invention changed the definition of what an artifact could do. Previously, only living things could modify their behavior in response to changes in the environment. Other examples of self-regulating feedback control systems include the steam engine governor, created by James Watt (1736–1819), and the thermostat, invented by Cornelis Drebbel (1572–1633), who also invented the submarine. The mathematical theory of stable feedback systems was developed in the 19th century. The central figure in the creation of what is now called control theory was NorbertCONTROLTHEORY Wiener (1894–1964). Wiener was a brilliant mathematician who worked with Bertrand Russell,amongothers,beforedeveloping aninterest inbiological andmechanical controlsystems andtheirconnection tocognition. LikeCraik(whoalsoused controlsystemsaspsychological models), Wiener and his colleagues Arturo Rosenblueth and Julian Bigelow challenged the behaviorist orthodoxy (Rosenblueth et al., 1943). They viewed purposive behavior as arising from a regulatory mechanism trying to minimize “error”—the difference between current state and goal state. In the late 1940s, Wiener, along with Warren McCulloch, Walter Pitts, and John von Neumann, organized a series of influential conferences that explored the new mathematical and computational models of cognition.
Wiener’s book Cybernetics (1948) became a bestseller and awoke the public to the possibility of artificially intelligent machines. Meanwhile, in Britain, W. Ross Ashby (Ashby, 1940) pioneered similar ideas. Ashby, Alan Turing, Grey Walter, and others formed the Ratio Club for “those who had Wiener’s ideas before Wiener’s book appeared.” Ashby’s Design for a Brain (1948, 1952) elaborated on his idea that intelligence could be created by the use of homeostatic devices containing appro-HOMEOSTATIC priate feedback loops to achieve stable adaptive behavior. Modern control theory, especially the branch known as stochastic optimal control, has asitsgoal the design ofsystems that maximize anobjective function overtime. Thisroughl matches our view of AI: designing systems that behave optimally. Why, then, are AI and control theory two different fields, despite the close connections among their founders? The answer lies in the close coupling between the mathematical techniques that were familiar to the participants and the corresponding sets of problems that were encompassed in each world view. Calculus andmatrixalgebra, thetools ofcontrol theory, lend themselves tosystems that aredescribable byfixedsetsofcontinuous variables, whereasAIwasfounded inpartasaway toescape fromthetheseperceived limitations. Thetools of logical inference andcomputation allowed AI researchers to consider problems such as language, vision, and planning that fell completely outside the control theorist’s purview.
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