![](bg4.png)
than by relying on pre-defined strategies. To this end,
we can leverage the vast literature of MAS learning
methods (Sen and Weiss, 1999) (Busoniu et al., 2009)
to use the learning services provided by the LL.
The Cognitive Layer (COG) is essential to close
and animate the loop in our architecture by orches-
trating the LL and the CL to drive the self-sustaining
capabilities of the robotic ecology and reduce its re-
liance from pre-programmed models. While the LN
provides learning functionalities that can be used to
fuse and enhance existing perception abilities and to
predict and classify events and human activities, each
of these learning tasks must be precisely pre-defined,
in terms of the data sources to be provided in input to
each learning module as well as the examples needed
for training their outputs. Similarly, while the CL can
synthesize and coordinate the execution of strategies
to achieve goals set for the whole ecology, its agents
must be explicitly tasked, e.g. by the user, or by avail-
ing of pre-programmed service rules.
The COG is being built using Self-Organizing
Fuzzy Neural Networks (SOFNNs) (Leng et al., 2004;
Prasad et al., 2010) where fuzzy techniques are used
to create or enhance neural networks and that can be
used to learn membership functions and create fuzzy
rules. Recently, research interests in self-organizing
neural network systems have moved on from param-
eter learning to the structure learning phase, with
a minimum of supervision. Our aim is to create
SOFNNs that reflects the knowledge being obtained
by the robotic ecology and autonomously map it to
goals to be achieved by the CL in order to satisfy
generic application requirements while also driving
active exploration to gather new knowledge. The par-
ticular appeal of SOFNNs is their ability for struc-
tural modification through neuron addition and prun-
ing. By linking such a structural adaptation to nov-
elty detection and habituation mechanisms (Mannella
et al., 2012), we aim to create a self-sustaining archi-
tecture that would start from using hand-coded neural
fuzzy rules but that would soon be able to leverage
past experiences to autonomously adapt them to the
context where the robotic ecology is installed.
4 CONCLUSIONS AND FUTURE
WORK
The goal of this position paper was to put forward the
concept of self-sustaining, learning robotic ecologies
as a powerful extension of traditional WSNs. It has
presented the rationale for the adoption and the inte-
gration of a number of techniques for the development
of adaptive applications using this concept. While
all the techniques illustrated in this paper have been
tested in isolation, we believe that their extension
and integration as discussed in this paper promises
to solve many of the problems that still obstruct the
implementation and diffusion of smart robotic envi-
ronments outside research laboratories. Future work
will refine and implement our proposed architecture
and exercise it in realistic settings.
ACKNOWLEDGEMENTS
This work is partially supported by the EU FP7 RU-
BICON project (contract no. 269914).
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