What is Artificial Intelligence?

By Jack Copeland

© Copyright B.J. Copeland, May 2000

Nouvelle AI

The approach to AI now known as "nouvelle AI" was pioneered at the MIT AI Laboratory by the Australian Rodney Brooks, during the latter half of the 1980s. Nouvelle AI distances itself from traditional characterisations of AI, which emphasize human-level performance. One aim of nouvelle AI is the relatively modest one of producing systems that display approximately the same level of intelligence as insects.

Practitioners of nouvelle AI reject micro-world AI, emphasising that true intelligence involves the ability to function in a real-world environment. A central idea of nouvelle AI is that the basic building blocks of intelligence are very simple behaviours, such as avoiding an object, and moving forward. More complex behaviours "emerge" from the interaction of these simple behaviours. For example, a micro-robot whose simple behaviours are (1) collision-avoidance and (2) motion toward a moving object will appear to chase the moving object while hanging back from it a little.

Brooks focussed in his initial work on building robots that behave somewhat like simplified insects (and in doing so he deliberately turned away from traditional characterisations of AI such as the one given at the beginning of this article). Examples of his insect-like mobile robots are Allen (after Allen Newell) and Herbert (after Herbert Simon). Allen has a ring of twelve ultrasonic sonars as its primary sensors and three independent behaviour-producing modules. The lowest-level module makes the robot avoid both stationary and moving objects. With only this module activated, Allen sits in the middle of a room until approached and then scurries away, avoiding obstacles as it goes. The second module makes the robot wander about at random when not avoiding objects, and the third pushes the robot to look for distant places with its sensors and to move towards them. (The second and third modules are in tension--just as our overall behaviour may sometimes be the product of conflicting drives, such as the drive to seek safety and the drive to avoid boredom.)

Herbert has thirty infrared sensors for avoiding local obstacles, a laser system that collects three-dimensional depth data over a distance of about twelve feet in front of the robot, and a hand equipped with a number of simple sensors. Herbert's real-world environment consists of the busy offices and work-spaces of the AI lab. The robot searches on desks and tables in the lab for empty soda cans, which it picks up and carries away. Herbert's seemingly coordinated and goal-directed behaviour emerges from the interactions of about fifteen simple behaviours. Each simple behaviour is produced by a separate module, and each of these modules functions without reference to the others. (Unfortunately, Herbert's mean time from power-on to hardware failure is no more than fifteen minutes, owing principally to the effects of vibration.)

Other robots produced by Brooks and his group include Genghis, a six-legged robot that walks over rough terrain and will obediently follow a human, and Squirt, which bides in dark corners until a noise beckons it out, when it will begin to follow the source of the noise, moving with what appears to be circumspection from dark spot to dark spot. Other experiments involve tiny "gnat" robots. Speaking of potential applications, Brooks describes possible colonies of gnat robots designed to inhabit the surface of TV and computer screens and keep them clean.

Brooks admits that even his more complicated artificial insects come nowhere near the complexity of real insects. One question that must be faced by those working in situated AI is whether insect-level behaviour is a reasonable initial goal. John von Neumann, the computer pioneer and founder, along with Turing, of the research area now known as "artificial life", thought otherwise. In a letter to the cyberneticist Norbert Wiener in 1946, von Neumann argued that automata theorists who select the human nervous system as their model are unrealistically picking "the most complicated object under the sun", and that there is little advantage in selecting instead the ant, since any nervous system at all exhibits "exceptional complexity". Von Neumann believed that "the decisive break" is "more likely to come in another theater" and recommended attention to "organisms of the virus or bacteriophage type" which, he pointed out, are "self-reproductive and ... are able to orient themselves in an unorganised milieu, to move towards food, to appropriate it and to use it". This starting point would, as he put it, provide "a degree of complexity which is not necessarily beyond human endurance".

The frame problem

The products of nouvelle AI are quite different from those of symbolic AI, for example Shakey and FREDDY. These contained an internal model (or "representation") of their micro-worlds, consisting of symbolic descriptions. This structure of symbols had to be updated continuously as the robot moved or the world changed. The robots' planning programs would juggle with this huge structure of symbols until descriptions were derived of actions that would transform the current situation into the desired situation. All this computation required a large amount of processing time. This is why Shakey performed its tasks with extreme slowness, even though careful design of the robot's environment minimised the complexity of the internal model. In contrast, Brooks' robots contain no internal model of the world. Herbert, for example, continuously discards the information that is received from its sensors, sensory information persisting in the robot's memory for no more than two seconds.

AI researchers call the problem of updating, searching, and otherwise manipulating, a large structure of symbols in realistic amounts of time the frame problem. The frame problem is endemic to symbolic AI. Some critics of symbolic AI believe that the frame problem is largely insolvable and so maintain that the symbolic approach will not "scale up" to yield genuinely intelligent systems. It is possible that CYC, for example, will succumb to the frame problem long before the system achieves human levels of knowledge.

Nouvelle AI sidesteps the frame problem. Nouvelle systems do not contain a complicated symbolic model of their environment. Information is left "out in the world" until such time as the system needs it. A nouvelle system refers continuously to its sensors rather than to an internal model of the world: it "reads off" the external world whatever information it needs, at precisely the time it needs it. As Brooks puts it, the world is its own best model--always exactly up to date and complete in every detail.

Situated AI

Traditional AI has by and large attempted to build disembodied intelligences whose only way of interacting with the world has been via keyboard and screen or printer. Nouvelle AI attempts to build embodied intelligences situated in the real world. Brooks quotes approvingly from the brief sketches that Turing gave in 1948 and 1950 of the "situated" approach. Turing wrote of equipping a machine "with the best sense organs that money can buy" and teaching it "to understand and speak English" by a process that would "follow the normal teaching of a child". Turing contrasted this with the approach to AI that focuses on abstract activities, such as the playing of chess. He advocated that both approaches be pursued, but until now relatively little attention has been paid to the situated approach.

The situated approach is anticipated in the writings of the philosopher Bert Dreyfus, of the University of California at Berkeley. Dreyfus is probably the best-known critic of symbolic AI. He has been arguing against the Physical Symbol System Hypothesis since the early 1960s, urging the inadequacy of the view that everything relevant to intelligent behaviour can be captured by means of structures (e.g. lists) of symbolic descriptions. At the same time he has advocated an alternative view of intelligence, which stresses the need for an intelligent agent to be situated in the world, and he has emphasised the role of the body in intelligent behaviour and the importance of such basic activities as moving about in the world and dealing with obstacles. Once reviled by admirers of AI, Dreyfus is now regarded as a prophet of the situated approach.

Cog

Brooks' own recent work has taken the opposite direction to that proposed by von Neumann in the quotations given earlier. Brooks is pursuing AI's traditional goal of human-level intelligence, and with Lynn Andrea Stein, he has built a humanoid robot known as Cog. Cog has four microphone-type sound sensors and is provided with saccading foveated vision by cameras mounted on its "head". Cog's (legless) torso is capable of leaning and twisting. Strain gauges on the spine give Cog information about posture. Heat and current sensors on the robot's motors provide feedback concerning exertion. The arm and manipulating hand are equipped with strain gauges and heat and current sensors. Electrically-conducting rubber membranes on the hand and arm provide tactile information.

Brooks believes that Cog will learn to correlate noises with visual events and to extract human voices from background noise; and that in the long run Cog will, through its interactions with its environment and with human beings, learn for itself some of the wealth of common sense knowledge that Lenat and his team are patiently hand-coding into CYC.

Critics of nouvelle AI emphasis that so far the approach has failed to produce a system exhibiting anything like the complexity of behaviour found in real insects. Suggestions by some advocates of nouvelle AI that it is only a short step to systems which are conscious and which possess language seem entirely premature.

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