Cognitive agents and machine learning by example: Representation with conceptual graphs

As machine learning (ML) and artificial intelligence progress, more complex tasks can be addressed, quite often by cascading or combining existing models and technologies, known as the bottom-up design. Some of those tasks are addressed by agents, which attempt to simulate or emulate higher cognitive abilities that cover a broad range of functions; hence, those agents are named cognitive agents. We formulate, implement, and evaluate such a cognitive agent, which combines learning by example with ML. The mechanisms, algorithms, and theories to be merged when training a cognitive agent to read and learn how to represent knowledge have not, to the best of our knowledge, been defined by the current state-of-the-art research. The task of learning to represent knowledge is known as semantic parsing, and we demonstrate that it is an ability that may be attained by cognitive agents using ML, and the knowledge acquired can be represented by using conceptual graphs. By doing so, we create a cognitive agent that simulates properties of learning by example, while performing semantic parsing with good accuracy. Due to the unique and unconventional design of this agent, we first present the model and then gauge its performance, showcasing its strengths and weaknesses.