The name is absent

intelligent⁴. From this perspective, researchers began to model intelligence based on behaviour,
instead of on knowledge, creating the so called behaviour-based systems (BBS) (Brooks, 1986;
Brooks, 1991).

Figure 3 shows a diagram of the issues discussed above. We perceive natural exhibitions
of intelligence (i.e. what we judge to be intelligent), and then we model it in a synthetic way
(Steels, 1995; Verschure, 1998; Castelfranchi, 1998). Our synthetic theory will help to explain
our perceptions if it is capable of reproducing what we perceive. For example, we will know
more about how language works if we build an artificial language module, or we will understand
more about perception if we engineer a robotic vision system, instead of “just” making theories
of them. We will understand more about intelligence as we build artificial intelligence. This
“synthetic method”, as described by Steels (1995), is different from the “inductive method”.
The inductive method observes facts, then makes a generalization or abstraction, to develop
a theory. The theory is used to predict facts, which are verified against observed facts, which
falsify or justify the theory. A diagram of this method can be seen in Figure 4. The synthetic
method, also generalizes or abstracts observed phenomena to produce a theory. Only that this
theory is used to engineer an artificial system, as a substitute of the natural system. This
artificial system is operated, and its performance is observed, which falsifies or justifies the
theory in dependance of how similar the observed performance is to the observed facts. A
diagram of this method can be seen in Figure 5. The idea of this method, is to build a “parallel”,
or artificial system, which should behave in a similar way than the natural system in which it was
inspired. If it does, it helps to comprehend the real system.