Integration of code-fragment based learning classifier systems for multiple domain perception and learning
Yi Liu, Muhammad Iqbal, Isidro M. Alvarez, Will Neil Browne · 2016
It has been shown that identifying building blocks of knowledge and then reusing them to solve complex problems is a practical and useful endeavor. Previous work made it possible to solve various, until then, intractable tasks. However, the individual algorithms targeted one specific problem type, e.g. scalable problems or domains with repeating patterns. The question that arises is: Can the disparate techniques be combined into a single approach to solve more complex problems that span several domains or that may be unknown to the agent? The first stage in developing such a system is to be able to recognise domains from unidentified input stimuli and identify the approaches best suited to them. The novel work here aims to realise this primary stage by combining several code-fragment (CF) based XCS systems. The stimulus and its guiding effect, will be instrumental in helping the agent decide which of its stored systems is the most capable of solving the problem, or if there is a conflict between possible solutions. Importantly, the agent will be capable of determining if the current problem is entirely new, in which case it spawns a training agent to produce a tractable solution to store and reuse. The proposed technique relies on the proven benefits in scalability of CF based systems and furthers the body of knowledge by tackling unknown problems (to the agent). The main contribution of this research is that a system of proven CF techniques is used for the first time. We show that by utilizing the new CF system, it is possible to identify an unknown problem and to arrive at a viable solution.