  Complex networks have been found to provide a good representation of the
structure of knowledge, as understood in terms of discoverable concepts and
their relationships. In this context, the discovery process can be modeled as
agents walking in a knowledge space. Recent studies proposed more realistic
dynamics, including the possibility of agents being influenced by others with
higher visibility or by their own memory. However, rather than dealing with
these two concepts separately, as previously approached, in this study we
propose a multi-agent random walk model for knowledge acquisition that
incorporates both concepts. More specifically, we employed the true self
avoiding walk alongside a new dynamics based on jumps, in which agents are
attracted by the influence of others. That was achieved by using a L\'evy
flight influenced by a field of attraction emanating from the agents. In order
to evaluate our approach, we use a set of network models and two real networks,
one generated from Wikipedia and another from the Web of Science. The results
were analyzed globally and by regions. In the global analysis, we found that
most of the dynamics parameters do not significantly affect the discovery
dynamics. The local analysis revealed a substantial difference of performance
depending on the network regions where the dynamics are occurring. In
particular, the dynamics at the core of networks tend to be more effective. The
choice of the dynamics parameters also had no significant impact to the
acquisition performance for the considered knowledge networks, even at the
local scale.
