consider the agent's personal history and thus better
capture the agent's individuality.
In its processes, the model respects long-term and
short-term links between concepts. Functionally the
model is partly based on Hopfield's auto-associative
memory and Hebb's rules of learning, whose it
modifies for given conditions and goals.
The model was tested on a dataset of annotated
images. The results demonstrate the model's ability to
perform associative reasoning and create a current
knowledge context usable for other agent processes.
That was the main goal of the work. The results also
show that the whole process is significantly affected
by global parameters, mainly forgetting coefficients
and limiting objects' activation when generating
context.
Future research will focus on further testing the
model, both in terms of its efficiency and
performance in processing large graph structures and
its compliance with the observed human behavior. In
a longer perspective, the goal is to use the model in
other projects focused on multiagent systems and
behavioral simulations.
Work on the model will also focus on the
possibilities of processing data of a different nature.
The annotation of the images probably best
demonstrates the model activity, but time series or,
more generally, data captured in relational structures
can also be processed. The model can then provide a
different view of this data and the possibilities of its
processing.
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