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In this presentation, I address two major questions:
1. What is Intelligent Agent Perception?
2. How do Intelligent Agents Perceive?
In addressing the meaning of agent perception, I highlight impediments to the perceptual process and the process of situation assessment.
In addressing how agents perceive, I highlight traditional approaches to robotic perception and then the next step after sensor input, which is how sensor data (vision sensor, tactile, olfactory, haptic, etc.) is interpreted. Methods for interpretation include solutions based on Bayes' Theorem, the underpinning of many robotics algorithms; Probabilistic Algorithms; and Artificial Neural Networks.
I also discuss a current system for robotic perception, designed to accommodate more robust and complex robotic needs: using sensors in tandem with machine learning. This method is closer to mimicking the human perceptual process than previous methods. I discuss some examples of this: 1) a study in which researchers used visual sensors and an artificial neural network (ANN) for robotic perception; 2) a study in which researchers used haptic sensors and a classification algorithm, called a boosting algorithm for robotic perception; and 3) a study in which researchers used pressure sensors, an ANN, and intended to add pre-programmed models in order to facilitate robotic perception.