MLR (Memory, Learning and Recognition): A General Cognitive Model -- applied to Intelligent Robots and Systems Control
It addresses the problem of integrating diverse research fields for intelligent control in robotics and systems, but appears incremental as it builds on existing concepts without major breakthroughs.
The paper introduces the Memory, Learning and Recognition (MLR) cognitive model to bridge gaps between robotics, AI, cognitive science, and neuroscience, aiming to define intelligence parametrically and create a general control model for robots and systems, with proof-of-concept demonstrated through small-scale experimentation.
This paper introduces a new perspective of intelligent robots and systems control. The presented and proposed cognitive model: Memory, Learning and Recognition (MLR), is an effort to bridge the gap between Robotics, AI, Cognitive Science, and Neuroscience. The currently existing gap prevents us from integrating the current advancement and achievements of these four research fields which are actively trying to define intelligence in either application-based way or in generic way. This cognitive model defines intelligence more specifically, parametrically and detailed. The proposed MLR model helps us create a general control model for robots and systems independent of their application domains and platforms since it is mainly based on the dataset provided for robots and systems controls. This paper is mainly proposing and introducing this concept and trying to prove this concept in a small scale, firstly through experimentation. The proposed concept is also applicable to other different platforms in real-time as well as in simulation.