1 research outputs found
Relevant Knowledge First - Reinforcement Learning and Forgetting in Knowledge Based Configuration
In order to solve complex configuration tasks in technical domains, various
knowledge based methods have been developed. However their applicability is
often unsuccessful due to their low efficiency. One of the reasons for this is
that (parts of the) problems have to be solved again and again, instead of
being "learnt" from preceding processes. However, learning processes bring with
them the problem of conservatism, for in technical domains innovation is a
deciding factor in competition. On the other hand a certain amount of
conservatism is often desired since uncontrolled innovation as a rule is also
detrimental. This paper proposes the heuristic RKF (Relevant Knowledge First)
for making decisions in configuration processes based on the so-called
relevance of objects in a knowledge base. The underlying relevance-function has
two components, one based on reinforcement learning and the other based on
forgetting (fading). Relevance of an object increases with its successful use
and decreases with age when it is not used. RKF has been developed to speed up
the configuration process and to improve the quality of the solutions relative
to the reward value that is given by users.Comment: pdf-file, 33 pages, 17 figure