Symbolic erformance & Learning in Continuous Environ ents
نویسنده
چکیده
We present an approach which enables an agent to learn to achieve goals in continuous environments using a symbolic architecture. Symbolic processing has an advantage over numerical regression techniques because it can interface more easily with other symbolic systems, such as systems for natural language and planning. Our approach is to endow an agent with qualitative “seed” knowledge and allow it to experiment in its environment. learned. The new action is the linear interpolation the two closest results straddling the current goal.
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تاریخ انتشار 1999