Winner-take-all is a computer science concept that has been widely applied in behavior-based robotics as a method of action selection for intelligent agents.
[1][2][3] In the 1980s and 1990s, many roboticists and cognitive scientists were attempting to find speedier and more efficient alternatives to the traditional world modeling method of action selection.
[4] In 1982, Jerome A. Feldman and D.H. Ballard published the "Connectionist Models and Their Properties", referencing and explaining winner-take-all as a method of action selection.
[clarification needed] Winner-take-all architectures for action selection soon became a common feature of behavior-based robots, because selection occurred at the level of the action modules (bottom-up) rather than at a separate cognitive level (top-down), producing a tight coupling of stimulus and reaction.
[8] Some critics of this model hold that any existing set of division rules for the predecessor and conflictor connections between modules produce sub-par action selection.
Defenders of these systems argue that forbidding world-modeling puts unnecessary constraints on behavior-based robotics, and that agents benefits from forming representations and can still remain reactive.