مشاهده مشخصات مقاله
Bayesian Approach to Learning Temporally Extended Concepts
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نویسنده (ها) |
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Saied Haidarian Shahri
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Farzad Rastegar
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Majid Nili Ahmadabadi
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مربوط به کنفرانس |
دوازدهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
In several previous studies it has been shown that the generalization capabilities of humans through concept
learning is reminiscent of Bayesian modeling. When discriminating concepts from one another, human subjects
tend to focus on the relevant features of the subspace and ignore the irrelevant ones. In this paper we propose a
Bayesian concept learning paradigm that utilizes unrestricted Bayesian networks to learn the required concepts
for optimal decision making. This approach has several beneficial characteristics that a concept learning
algorithm should hold. At first it can both learn form observing an expert performing the desired task and from
its own experience while carrying it out. Secondly, it is a close and computationally feasible approximation to the
Bayesian modeling capabilities of humans. Thirdly, the Markov blanket surrounding the decision variable can
render the irrelevant features independent and therefore this approach can ignore them seamlessly from the
feature subspace. The simulation and experimental results are promising and show that our approach can
successfully extract the required temporally extended concepts for a mobile robot task. |
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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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خرید مقاله
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