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Page 11 de Silva. Intell Robot 2021;1(1):3-17 https://dx.doi.org/10.20517/ir.2021.01

Figure 5. The architecture of a convolutional neural network.

(similar to what Expert Systems provide). In Edge AI, AI algorithms are processed locally on a hardware
device. The algorithm uses data created on the device (e.g., data generated by the algorithm) and other data
(external data, including those from sensors and through the system interface). Hence, Edge AI functions at
the “edge of the system network”. Fuzzy logic attempts to be similar to human decision-making by
incorporating “fuzzy” or “qualitative” or “approximate” data, such as those that use qualifiers like fast, small,
better, and close. Qualitative or fuzzy reasoning is used in the decision-making (inference) process. Swarm
Intelligence behaves like a swarm of animals or insects. They are distributed (not hierarchical) and interact
with each other to learn and make decisions. The members in a swarm use very simple rules, yet leading to
“intelligent” global behavior, even though an individual member is not quite intelligent, which will improve
with time. Evolutionary computing relies on genetic algorithms or genetic computing to realize “optimized”
behavior through learning. The basis of this methodology is biological evolution (or survival of the fittest).

Within AI, apart from “learning”, other characteristics of intelligence need to be investigated and
incorporated (e.g., decision making with partial, approximate or qualitative information, use of
“expectational knowledge”, various approaches of reasoning such as inductive reasoning, ability to deal with
unfamiliar situations, common sense, inventiveness, self-awareness, attention representation, and
classification). Under machine learning itself, many methods exist, such as CNN, dynamic or recurrent
neural networks (RNN), reinforcement learning, support vector machines, and entropy-based approaches.
The rationalization of why a particular learning method is chosen (justification) should be a requirement in
any application. Comparative evaluations of different methods should be carried out, with a proper
reference (basis) for comparison. In this manner, comparative advantages and disadvantages of different
methods should be determined, the rationale for choosing a particular approach for the application. When
machine learning is applied in a particular situation, domain transformation or domain adaptability needs
consideration because the domain of learning is typically not the same as the domain of application .
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5. ROBOTIC CONTROL
It should be clear that proper control techniques are crucial for the effective operation of a robotic system.
Typically, different types of multiple robots are used in practical applications. Then, networked and
automated or autonomous operation of multiple robots, in a common, self-adaptive, and intelligent system
architecture, implemented on a common platform, with resource sharing, has to be implemented.

The networked operation of multiple robots and other agents (sensors, actuators, controllers, and other
devices) is not new. Furthermore, system optimization, intelligent systems, and adaptive control have been
extensively investigated and applied by us and others. In this backdrop and the strong foundation of prior
work, the networked implementation of multiple robots (and other agents) may focus on the following
aspects: