Structural Language

1. Structural Language
Benjamin Yiwen Faerber
January 19, 2017
Language can be understood as a communication of sequences of signs. What is con-
sidered as data science should be envisaged as the interpretation of sequences of data. In
this regard every species has a language as all communicate sequences of signs. This in-
terpretation can be grasped best with a computer game in which two fighters fight against
each other. Each fighter has a repertoire of fight moves and applies a sequence of moves
to win the contest. So for example fighter 1 has moves A, B, C, D available and fighter 2
has moves repertoire E, F, G, H. Fighter 1 applies e.g. sequence seq1 = A, C, D, B and
fighter 2 applies sequence seq2 = H, E, F, G. To understand the outcome of this simple
communication signs can be paired: (A, H), (C, E), (D, F), (B, G). Crickets in the rain for-
est make synchronous sounds but what are the individual sounds which one insect makes.
Does the individual chirp randomly or in accordance with the group? So here we would have
hundreds of individual insects making sound sequences which then sound synchronous in a
swarm. Birds are much easier to understand, as one bird in the forest makes one sequence
of tones which is then responded by one other bird with a sequence of tones. Obviously the
birds can communicate with each other. The key to other languages is statistical analysis.
What are the aims of communication between two sequences? One simple answer as given
in this video game depicted above is which wins. But in other cases collaboration for a
common objective are mostly possible. After all two humans communicating with each
other are mostly not in a fight as are also not birds. To make a statistical analysis one has
to understand language as composed of structures: Structures in the sense of sequences of
symbols. Also without knowing the meaning of one symbol or tone, one can at least find
repetitions in structures and compare the structures with each other. I even postulate that
from the structure one can even derive content, or at least assess the depth of the content.
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2. 1 Some statistical analysis
If one can estimate the order of the symbols of a repertoire of one agent, like for example the
strength of the fighting moves of one fighter or the pitch of the tones of the birds, one can
make correlation analyses. For example a bird has tones A, B, C, D where A > B > C > D
or the fighter in the computer game has moves A, B, C, D where A > B > C > D one can
introduce the Kendall Tau and the Spearman coefficient for statistical analysis by looking
at the pairings, e.g. (A, H), (C, E), (D, F), (B, G).
rR
XY = 1 −
6 n
i=1 (R (xi) − R (xi))2
(n (n2 − 1))
(1)
rT
XY =
C − D
n (n − 1) /2
(2)
where C is the number of concordant pairs and D is the number of discordant pairs. A pair
is concordant if
xi > xj, yi > yj
or
xi < xj, y <i< yj
A pair is discordant if
xi > xj, yi < yj
or
xi < xj, y <i> yj
2 Computer games
In today’s computer games one has incorporated very little artificial intelligence. The
reaction of the computer playing against a human is mostly based on a set of given rules
for each circumstance. To make the game more complex one can incorporate behavioral
dynamics.
Figure 1: The guards in this game follow basic situation based rules.
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3. This would mean a set of basic rules for each situation with which the computer reacts
on human interaction. But the next step would be to rely on sequences: Sequences of
decisions made by the computer and not decisions relying on the situation. In this way the
”agent” becomes completely autonomous. And the situation for the computer player is no
longer assessable.
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