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a goal is constructed arbitrarily, then searching through all of possible means is the only
method for selecting one or several means appropriate to achieve the goal. The efficacy of
those means may increase the probability of their usage in similar situations; however, this
class does not suggest unequivocal methods based on feedback loops to construct novel
means. Various, largely symbolic, systems can be related to this class (Bertino, Piero &
Zarria, 2001; Jackson, 1998; Newell, 1990).
Because these classes are so obvious, it is very reasonable to assume that any AI project can
be attributed to one of the classes (though some projects may combine characteristics of
both). Like other technical systems, AI projects are not intended to imitate their natural
counterparts but rather attempt to achieve “natural functionality”. The fact that the objective
of Artificial Intelligence as a scientific and engineering activity is the full-scale functionality
of human intelligence means AI researchers implicitly suggest that humans can be
attributed to one or both of these classes. However, in my opinion this supposition is
doubtful.
Undoubtedly, like other animals humans have a complex structure of innate goals
associated with survival and reproduction. As a result, some scholars attribute humans to
the first class of goal-directed systems. For example, behaviorism suggested an innate
motivation mechanism in order to establish connections between goals and means through
reward and punishment (Heckhausen, 1980). Currently, evolutionary psychology is very
explicit in supposing that humans have an innate repertoire of goals and domain-specific
modules (Tooby &Cosmides 1992; Tooby, Cosmides & Barrett, 2005). However, the
attribution of humans to the first class system is unable to explain the diversity and rapid
alterations of actions either at the level of a single individual, or at that of a whole society
(Buller, 1998).
This inability hints that humans belong to the second-class systems. The main problem,
which faces such systems, is a combinatorial explosion owing to the need to search through
the potentially infinite number of possible means. However, people regularly make effective
and flexible decisions without being overwhelmed by their decision-making processes.
Some ideas to explain how the mind avoids a combinatorial explosion have been suggested
(Newell, 1990), but they do not seem satisfactory (Cooper & Shallice, 1995). Moreover,
although people are able to apply the strategy of deliberately searching among several
conscious alternatives, some problems demonstrate that the thinking system is reluctant to
use searching.
Consider, for example the following simplest chess riddle: White: Ke1, Rf2, Rh1; Black: Ka1.
White to play and mate in one. When the author (P.P.) was acquainted with the problem he
found that many poor chess players (and P.P. himself), who were, of course, familiar with
the chess rules, could not solve the riddle or solved it after many attempts. However, any
chess program immediately finds the solution: castling O-O. Indeed, since White should
mate in one, in order to solve this problem it is necessary to generate each formally possible
move for White in the given position, and to test whether this move is the solution. Such a
searching procedure is available for the computer program but often not available for a
human.
Everyday experience seems to demonstrate that people seldom use searching among
possible alternatives. Instead, they prefer (often unconsciously) a routine action. In
