Artificial General Intelligence via Finite Covering with Learning

This position paper claims that the combination of solutions to a finite collection of problem instances and an expansion capability of those solutions to similar problems is enough to achieve the artificial general intelligence comparable to the human intelligence. Learning takes place during expansion of existing solutions using various methods such as trial and error, generalization, case-based reasoning, etc. This paper also looks into the amount of innate problem solving capability an artificial agent must have and the difficulty of the tasks the agent is expected to solve. To illustrate our claim examples in robotics are used where tasks are physical movements of the agent and objects in its environment.