Bootstrapping is a widely employed technique in the process of building highly complex systems such as microprocessors, language compilers, and computer operating systems. It could play an even more prominent role in the creation of computation systems capable of supporting intelligent agent behaviors because of the even higher level of complexity. The prospect of a self-bootstrapping, self-improving intelligent system has motivated various fields of research in machine learning. However, a robust, generalizable methodology of machine learning is yet to be found; there are still a lot of learning behaviors that no existing learning technique can adequately account for. We believe a uniform, logic-based system such as active logic [1, 2], will be more successful in the realization of this ideal. The overall architecture that we envision is as follows: a central commonsense reasoner module attends to novel situations where the system does not already have expertise, and to its own failures; it then reasons its way to solutions or repairs, and puts these into action while at the same time causing "expert" modules to be either created or retrained so as to more quickly enact those solutions on future occasions. Thus what we propose is a kind of meld between declarative and procedural techniques where the former has great expressive power and flexibility (but is slow) and the latter is very fast but hard to adapt to new situations .We will explore the possibilities of using reflection and continual computation toward this end.
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