| Summary: | In the recent decades, alternative notions regarding the role of symbols in
intelligence in natural and artificial systems have attracted significant inter-
est. The main difference of the so-called situated and embodied approaches to
cognitive science from the traditional cognitivist position is that symbolic repre-
sentations are viewed as resources, similar to maps used for navigation or plans
for activity, instead of as transparent stand-ins in internal world models. Thus,
all symbolic resources have to be interpreted and re-contextualized for use in
concrete situations. In this view, one of the primary sources of such symbolic
resources is language. Cognitivism views language as a vessel carrying informa-
tion originally located in the processing mechanisms of the individual agents.
Situated approaches, on the other hand, view language both as a communicative
mechanism and as a means for the individual agents to enhance and extend their
cognitive machinery, by e.g. better utilizing their attentional resources, or mod-
ifying their perceptual-motor means. Taking inspiration from these ideas, and
building on multi-agent models developed in other fields, the field of language
evolution developed models of the emergence of shared resources for communi-
cation in a community of agents. In these models, agents with various means
of categorization and learning engage in communicative interactions with each
other, using shared signs to refer either to pre-given meanings or entities in a
situation. In order to avoid falling into the same mentalist pitfalls as cognitivism
in the design of these models, such as the stipulation of an inner sphere of mean-
ings for which communicative signs are mere labels, the role of communication
should be viewed as one of the social coordination of behavior using physically
grounded symbols. To this end, an experimental setup for language games, and
a robotic model for agents which engage in such games are presented. The
setup allows the agents to utilize shared symbols in the completion of a simple
task, with one agent instructing another on which action to undertake. The
symbols used by agents in the language games are grounded in the embodied
choices presented to them by their environment, and the agents can further use
the symbols created in these games for enhancing their own behavioral means.
The learning mechanism of the agents is similarity-based, and uses low-level
sensory data to avoid the building in of features. Experiments have shown that
the establishment of a common vocabulary of labels depends on how well the
instructors are trained on the task and the availability of feedback mechanisms
for the exchanged labels.
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