Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

model of cognition (vide fig. 2.12 ) reveals that there exist 5 mental states and 3
possible cycles that an intelligent agent can execute. The task of the agent, in
the present context, is to maintain a transition of states by reasoning and
learning paradigms. The reasoning schemes provide the agent new inferences,
abstracted from sensory data and knowledge. It is capable of deriving
inferences even in the absence of complete data and knowledge bases. The
learning schemes, on the other hand, help the agent by providing him
necessary actuating signals, when excited with sensory signals. The agent
thus is able to maintain state-transitions with the reasoning and the learning
modules. The book provides a detailed analysis of the mathematical models
that can be employed to design the reasoning and learning schemes in an
intelligent agent.
It must be added here that the model of cognition (vide fig. 2.12) is a generic
scheme and the whole of it need not be realized in most intelligent agents. We
now list some of the possible realization of the agents.
Pattern Recognition Agent: A pattern recognition agent receives
sensory signals and generates a desired pattern to be used for some
definitive purposes. For example, if the agent is designed for speaker
recognition, one has to submit some important speech features of the
speakers such as pitch, format frequencies, etc. and the system would
be able to give us the speaker number. If the recognition system is to
recognize objects from their visual features, then one has to submit
some features such as the largest diagonal that the 2-D image can
inscribe, the smallest diagonal that it can inscribe and the area of the 2-
D image surface. In turn, the system can return the name of the 2-D
objects such as ellipse, circle, etc. It may be mentioned here that a
pattern recognition system realizes only the sensing-action cycle of
the cognition.
A Path Planning Agent: A path planning agent perhaps is one of
the complete agents that uses all the states in the elementary model of
cognition (fig. 2.12). Such agents have ultrasonic sensors / laser
range finders by which it can sense obstacles around it. It saves the
sensed images in its short term memory and then extracts knowledge
about the possible locations of the obstacles. This is referred to as the
obstacle map of the robot’s environment. For subsequent planning
and the action cycle, the robot may use the obstacle map. Details of
the path planning scheme of the mobile robot will be presented in
chapter 24.