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de Silva. Intell Robot 2021;1(1):3-17 https://dx.doi.org/10.20517/ir.2021.01 Page 10

Figure 4. A model for the dynamics of intelligence.

learning alone will be able to achieve all forms of human intelligence in a robotic device.

4.2. Artificial intelligence
AI uses formal techniques to acquire some characteristics of intelligence. Models of AI are used for this
purpose based on one or more of the mentioned characteristics. Such approaches (or models) of AI include
Machine Learning, a very popular approach to AI. The conventional models of AI include knowledge-based
systems, soft computing (consisting of neural networks - NN, fuzzy systems, and evolutionary computing;
see for instance), and swarm intelligence. A knowledge-based system typically consists of a knowledge
[9]
base (or a rule base), a database, and an inference engine (the decision-maker). The decisions are made as
follows: some data in the database (including what is acquired recently through sensors) is matched with the
(context of the) rules in the knowledge base by the inference engine, and the inferences (or actions) are
determined accordingly (i.e., rules are fired). Popular “Expert Systems” are based on this model. Of course,
the knowledge base will be improved and enhanced continuously through “learning” and experience (so
machine learning is used here as well).

Deep learning is a popular approach to machine learning. It incorporates an intelligent and intensive
method of learning and sophisticated computing power that is available with such advancements as graphic
processing units and tensor processing units to process massive quantities of data efficiently. Deep learning
need not be limited to the use of deep NN but is the current trend. Deep NN includes Convolutional NN or
convolutional neural network (CNN) [4,10] (see Figure 5). They have a structure of multiple layers
(convolution layers) incorporating the “dynamic” learning ability and ending with a “Softmax” layer, which
is the classification layer. First, the NN is trained using “labeled data” (i.e., input data whose proper
outcomes are known a priori). Then, after the network is trained properly, unlabeled data (or new data) may
be used for actual decision-making. Thus, massive amounts of data, including sensed data (a mixture of
labeled and unlabeled data), may be effectively used in a deep NN.

Reinforcement learning relies on rewarding the correct decisions and penalizing the wrong decisions to
learn the proper decision strategies. AI agents are capable of providing explanations for their decisions