but progress was uneven. Programs that produced deductive proofs of logical or mathematical truths, such as those a human mathematician might derive, worked well, perhaps because they were more or less natural extensions of computer processing. But results in machine translation of language were discouraging; available methods could not cope with the subtleties of multiple and contextual meanings of words.And although an AI program could play a good game of check- ers, chess was too much. (Even long after these early efforts, in 1997, the chess-playing Deep Blue computer depended on brute force rather than subtle AI-based strategic analysis, using its great speed and memory to examine all possible outcomes of a given move and then selecting the best one.)
In these early approaches, the idea was to put into the computer a complete model of a system in symbolic terms that the computer could incorporate and apply. But it became clear that this “top-down” or “symbolic AI” method was not necessarily the best technique for robots operating in the real world. One famous example of the sym- bolic approach comes from work on robotic vision carried out from 1968 to 1972 at the Stanford Research Institute (now SRI Interna- tional). Funded by the Department of Defense, the plan was to make a robot that could autonomously traverse a battlefield to deliver sup- plies and gather fire-control information. The test unit (dubbed “Shakey” because it wobbled as it moved) consisted of a motorized wheeled platform with a computer, a TV camera for vision, a rangefinder to measure distance to an object, and a radio link to a second, remote, computer for more processing capacity. The robot was developed in an idealized environment, a set of rooms containing simple, brightly colored shapes such as cubes.
Shakey would receive a typed command such as “Find the cube- shaped block in that room and push it to the other room.”The robot would examine the room and the objects in it, identify the target, plan a route that avoided obstacles, and carry out the planned moves. Within this laborious process, Shakey displayed flashes of intelligence that combined perception, problem-solving capability, and the ability to move to the right place. In one trial, Shakey shifted a ramp so that the robot could roll up it to reach a target on a raised platform. But