مشاهده مشخصات مقاله
A Maze Solver based on a New Architecture of XCS
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نویسنده (ها) |
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مربوط به کنفرانس |
دوازدهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
Learning capabilities of an agent relies on the way that agent perceives the environment. When the agent’s
sensations convey only partial information about the environment, there may be different situations that appear
identical to the agent but require different actions to behave optimally. In this paper, we propose a new
approach to improve XCS’s performance in Partially Observable Markov Decision Process (POMDP) using a
newly introduced method to detect aliased states in the current environment. In our approach, at the initial state,
there exists only a single main XCS which handles all of the environmental states. When an existing aliased state
is detected using a simple mechanism, the system creates a new XCS, in addition to the main XCS which we call
Cooperative XCS. The new XCS is responsible for handling this detected state. This mechanism allows the main
XCS to handle non-aliased states and the other XCS’s cooperate with it by handling existing aliased states
independently. Thus, the system is called Cooperative Specialized XCS and its performance is compared with
some other classifier systems in some benchmark problems. The presented results demonstrate the effectiveness
of our proposed approach. |
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قیمت |
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برای اعضای سایت : ۱٠٠,٠٠٠ ریال
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برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
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برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
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