Chapter 10. ML: Machine Learning and Agents
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10.5.4 Distributed Learning: Case Study
To evaluate the distributed learning approach, an extensive MAS simulation
was performed. The simulation assumes a spatially two-dimensional sensor
network (see Figure 10.6 for details) with nodes arranged in a mesh grid con-
necting each node with up to four neighbour nodes. Each sensor node is
attached to a strain gauge sensor used to measure strain of an artificial plate.
The artificial sensor values were derived by inverse numerical computation
and transferred to the MAS simulation. Some simulation results are shown in
Figure 10.8. The agent population plots show the efficient data processing of
the event-based sensor processing and learning activities performed by the
agents.
Each learning/classification run requires about 0.5-1MB communication
costs (using code compression) in the entire network only, and the agent pop-
ulation reaches up to 400 agents (peak value, but executed in the simulation
by one physical JAM node), and a logical JAM node is populated with up to 10
agents.
10.6 Incremental Learning
The Machine Learning approaches presented in the previous section oper-
ate in two sequentially phases: (1) Learning (Deriving the prediction model
with known training data) and (2) Application (Performing the classification
using unknown data). Jiang [JIA13] showed that it is possible to perform incre-
mental learning at run-time using trees, very attractive for agent and SoS
approaches. A learned model (carried by the learner agent) is used to map
data vectors (of an input variable set x
1
,x
2
,..; the feature vector) on class val-
ues (of an output variable y). The model can be updated at run-time by adding
new training data or by updating the learned model by back propagation and
reinforcement learning. The classification tree consists of nodes testing a spe-
cific feature variable, i.e., a particular sensor value, creating a path to the
leaves of the tree containing the classification result, e.g., a mechanical load
situation. Among the distribution of the entire learning problem, event-based
activation of learning instances can improve the system efficiency significantly
as shown in the previous section, and can be considered as part of the distrib-
uted learning algorithm (a pre-condition). Commonly the locally sampled
sensor values are used for an event prediction, waking up the learner agent,
which collect neighbourhood data by using a divide-and-conquer system with
explorer child agents.
Combining the previously introduced distributed learning approach with
incremental learning algorithms enables a self-adaptive learning system with
a feedback loop, suitable for sensor processing, e.g., by integrated sensor net-
works in structural monitoring or by wide-area sensor networks.
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)