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Using Multi-Agent Networks to Manage Dynamic Changes 279

Finally, they have a set of intentions, which consist of the actions they are
currently performing to achieve their desires (Stone, 2007). This model is
based on a theory of how humans operate. It involves an information-
collection stage that forms the agent’s beliefs and a planning stage that
generates intents based on the agent’s desires. Planning is a continuous activ-
ity that generates a new set of intentions at each time step.
The difficulties with realizing the BDI model is in how each agent state is
represented and what reasoning mechanisms are utilized in the planning
stage. In the meta-agent approach, agent intents result in the creation of
other agents that provide a needed service. This process is true until we reach
the leaf nodes where, for the first time, agents are able to interact with IoT
devices to collect data.
One can see that as we move along the holonic pyramid, varying levels of
sophistication are needed in an agent’s reasoning mechanism. We can expect
top-level agents to have a fairly complex reasoning capability, while agents at
the bottom of the pyramid have little or no need for reasoning (Gerber,
Siekmann, & Vierke, 1999). The challenge then is to utilize a scalable rea-
soning mechanism that can support these varying levels of complexity.
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A behavior tree (BT) (Colledanchise & Ogren, 2018) is a graph-based
model of plans of execution that originated in the game industry. They have
recently received significant attention in the academic community due to
some very desirable properties. First, BTs have a small computational foot-
print. Second, BTs are able to emulate many other behavior representations
including: finite state machines, hierarchical finite state machines, subsump-
tion architectures, teleo-reactive programs, and decision trees. The major
advantage of BTs for our use is that they are highly modular, reusable,
and hierarchical. The implications for a holonic system is that the reasoning
exhibited by the holonic system can be a composition of reasoning modules
contained in ach agent comprising the system. Furthermore this configura-
tion supports the idea of scalability in agent reasoning.

14.6 CHALLENGES AND CONCLUSION

Implementing a meta agent–based EC approach for IoT based on the BDI
model introduces a number of difficult questions. First, how do agents
become aware of the IoT resources available to them? There are currently
multiple efforts underway to build open standards for IoT device discovery.
Most notable are UPnP and HyperCat. These are relatively low-level stan-
dards that generally do not provide a semantic-based service for SOA systems.