Problem-Solving and Learning Strategies Within the Independent Core Observer Model (ICOM) Cognitive Architecture

This paper primarily articulates the functionality of Problem-solving and learning strategies in the Independent Core Observer Model (ICOM) Cognitive architecture and, therefore, presents some components of the learning system within the Independent Core Observer Model (ICOM) cognitive architecture as applied to the observer side of the architecture. ICOM is uniquely designed to continuously enhance its problem-solving capabilities through a mechanism that integrates feedback from past experiences to optimize future actions, generate new actions, and extend the system functionality on the fly to perform new functions as the system determines is needed. Central to this system are problem identification, proposed solution generation, and implementing solutions through testable models of an action that directly inform new task achievements or goal settings related to those tasks. By embedding this functionality within the ICOM architecture, we enable the system to adapt and extend its functionality dynamically over time. Integrating continuous learning and adaptation with the rest of the system provides a powerful tool for self-evolving artificial intelligence systems. This approach seems to improve the system's efficiency and effectiveness in handling diverse and unforeseen challenges.