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公开(公告)号：US20110131163A1
公开(公告)日：2011-06-02
申请号：US12628421
申请日：2009-12-01
摘要： Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction.
摘要翻译： 描述专家组合的描述，专家可能是例如，自动化专家或人类专家。 在一个实施例中，选择引擎从专家组合中选择专家，并将专家分配给指定的任务。 例如，选择引擎具有贝叶斯机器学习系统，每当观察到任务上的专家表现时，该学习系统被迭代地更新。 例如，将稀疏活动的二进制任务和专家特征向量输入到使用机器学习系统学习的映射将这些特征向量映射到多维特征空间的选择引擎。 在示例中，映射向量的内积给出了对专家性能的概率分布的估计。 在一个实施例中，专家是自动化问题解决者，并且任务是诸如约束满足问题或组合拍卖之类的硬组合问题。
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公开(公告)号：US08433660B2
公开(公告)日：2013-04-30
申请号：US12628421
申请日：2009-12-01
摘要： Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction.
摘要翻译： 描述专家组合的描述，专家可能是例如，自动化专家或人类专家。 在一个实施例中，选择引擎从专家组合中选择专家，并将专家分配给指定的任务。 例如，选择引擎具有贝叶斯机器学习系统，每当观察到任务上的专家表现时，该学习系统被迭代地更新。 例如，将稀疏活动的二进制任务和专家特征向量输入到使用机器学习系统学习的映射将这些特征向量映射到多维特征空间的选择引擎。 在示例中，映射向量的内积给出了对专家性能的概率分布的估计。 在一个实施例中，专家是自动化问题解决者，并且任务是诸如约束满足问题或组合拍卖之类的硬组合问题。
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