Simulation with Societies of Behaviors
Norman I. Badler, Cary B. Phillips, Bonnie Webber · Oxford University Press eBooks · 1993
Recent research in autonomous robot construction and in computer graphics animation has found that a control architecture with networks of functional behaviors is far more successful for accomplishing real-world tasks than traditional methods. The high-level control and often the behaviors themselves are motivated lay the animal sciences, where the individual behaviors have the following properties: . . .• they are grounded in perception. . . . . . . • they normally participate in directing an agent’s effectors. . . . . . . • they may attempt to activate or deactivate one-auother. . . . . . . • each behavior by itself performs some task useful to the agent. . . . In both robotics and animation there is a desire to control agents in environments, though in graphics both are simulated, and in both cases the move to the animal sciences is out of discontent with traditional methods. Computer animation researchers are discontent with direct kinematic control and are increasingly willing to sacrifice complete control for realism. Robotics researchers are reacting against the traditional symbolic reasoning approaches to control such as automatic planning or expert systems. Symbolic reasoning approaches are brittle and incapable of adapting to unexpected situations (both advantageous and disastrous). The approach taken is, more or less, to tightly couple sensors and effectors and to rely on what Brooks [Bro90] calls emergent behavior, where independent behaviors interact to achieve a more complicated behavior. From autonomous robot research this approach has been proposed under a variety of names including: subsumption architecture by [Bro86], reactive planning by [GL90, Kae90], situated activity by [AC87], and others. Of particular interest to us, however, are those motivated explicitly by animal behavior: new AI by Brooks [Bro90], emergent reflexive behavior by Anderson and Donath [AD90], and computational neuro-ethology by Beer, Chiel, and Sterling [BCS90]. The motivating observation behind all of these is that even very simple animals with far less computational power than a calculator can solve real world problems in path planning, motion control, and survivalist goal attainment, whereas a mobile robot equipped with sonar sensors, laser-range finders, and a radio-Ethernet connection to a, Prolog-based hierarchical planner on a supercomputer is helpless when faced with the unexpected.