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    • 4. 发明授权
    • Belief tracking and action selection in spoken dialog systems
    • 在口语对话系统中的信念跟踪和动作选择
    • US08676583B2
    • 2014-03-18
    • US13221155
    • 2011-08-30
    • Rakesh GuptaDeepak RamachandranAntoine RauxNeville MehtaStefan KrawczykMatthew Hoffman
    • Rakesh GuptaDeepak RamachandranAntoine RauxNeville MehtaStefan KrawczykMatthew Hoffman
    • G10L15/22
    • G10L15/22
    • An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.
    • 响应于用户的说话话语,在口语对话系统中执行动作。 检索或创建将用户意图的信念状态映射到动作的策略。 信仰状态是根据口语说出来确定的,并且基于确定的信念状态和策略选择动作。 该动作被执行,并且在一个实施例中,涉及请求澄清来自用户的说话话语。 创建策略可以包括模拟用户输入和对话系统交互,并且迭代地修改策略参数,直到满足策略阈值。 在一个实施例中,通过将口语发音转换成文本来确定置信状态,将文本分配给与概率本体树(POT)中的节点相关联的一个或多个对话时隙,以及基于概率分布表中的概率分布表确定联合概率 POT和对话框插槽分配。
    • 8. 发明授权
    • Toro: tracking and observing robot
    • 托罗:跟踪和观察机器人
    • US08077919B2
    • 2011-12-13
    • US12249849
    • 2008-10-10
    • Rakesh GuptaDeepak Ramachandran
    • Rakesh GuptaDeepak Ramachandran
    • G06K9/00G06F11/00
    • G06K9/00295G06T7/143G06T7/277G06T2207/10016
    • The present invention provides a method for tracking entities, such as people, in an environment over long time periods. A region-based model is generated to model beliefs about entity locations. Each region corresponds to a discrete area representing a location where an entity is likely to be found. Each region includes one or more positions which more precisely specify the location of an entity within the region so that the region defines a probability distribution of the entity residing at different positions within the region. A region-based particle filtering method is applied to entities within the regions so that the probability distribution of each region is updated to indicate the likelihood of the entity residing in a particular region as the entity moves.
    • 本发明提供了一种用于在长时间的环境中跟踪诸如人之类的实体的方法。 生成基于区域的模型来模拟关于实体位置的信念。 每个区域对应于表示实体可能被找到的位置的离散区域。 每个区域包括一个或多个位置,其更精确地指定区域内的实体的位置,使得该区域定义驻留在区域内的不同位置的实体的概率分布。 基于区域的粒子滤波方法被应用于区域内的实体,使得每个区域的概率分布被更新,以指示实体在实体移动时驻留在特定区域中的可能性。
    • 9. 发明授权
    • Responding to situations using multidimensional semantic net and Bayes inference
    • 响应使用多维语义网和贝叶斯推理的情况
    • US07725418B2
    • 2010-05-25
    • US11046343
    • 2005-01-28
    • Rakesh GuptaVasco Calais Pedro
    • Rakesh GuptaVasco Calais Pedro
    • G06N5/02
    • G06N5/04
    • A system, apparatus and application for providing robots with the ability to intelligently respond to perceived situations are described. A knowledge database is assembled automatically, based on distributed knowledge capture. The knowledge base embodies the “common sense,” that is, the consensus, of the subjects who contribute the knowledge. Systems are provided to automatically preprocess, or “clean” the information to make it more useful. The knowledge thus refined is utilized to construct a multidimensional semantic network, or MSN. The MSN provides a compact and efficient semantic representation suitable for extraction of knowledge for inference purposes and serves as the basis for task and response selection. When the robot perceives a situation that warrants a response, an appropriate subset of the MSN is extracted into a Bayes network. The resultant network is refined, and used to derive a set of response probabilities, which the robot uses to formulate a response.
    • 描述了一种用于向机器人提供智能响应感知情况的能力的系统,设备和应用。 基于分布式知识捕获,自动组合知识数据库。 知识库体现了贡献知识的科目的“常识”,即共识。 提供系统以自动预处理或“清理”信息,使其更有用。 这样精炼的知识被用来构建一个多维语义网络或MSN。 MSN提供了一种适用于推理目的的知识提取的紧凑有效的语义表示,并且作为任务和响应选择的基础。 当机器人感知到需要响应的情况时,将MSN的适当子集提取到贝叶斯网络中。 所得到的网络被改进,并且用于导出机器人用于制定响应的一组响应概率。
    • 10. 发明授权
    • Meta learning for question classification
    • 元分析问题分类
    • US07603330B2
    • 2009-10-13
    • US11410443
    • 2006-04-24
    • Rakesh GuptaSamarth Swarup
    • Rakesh GuptaSamarth Swarup
    • G06N3/02G06N3/08
    • G06N99/005G06N3/0454
    • A system and a method are disclosed for automatic question classification and answering. A multipart artificial neural network (ANN) comprising a main ANN and an auxiliary ANN classifies a received question according to one of a plurality of defined categories. Unlabeled data is received from a source, such as a plurality of human volunteers. The unlabeled data comprises additional questions that might be asked of an autonomous machine such as a humanoid robot, and is used to train the auxiliary ANN in an unsupervised mode. The unsupervised training can comprise multiple auxiliary tasks that generate labeled data from the unlabeled data, thereby learning an underlying structure. Once the auxiliary ANN has trained, the weights are frozen and transferred to the main ANN. The main ANN can then be trained using labeled questions. The original question to be answered is applied to the trained main ANN, which assigns one of the defined categories. The assigned category is used to map the original question to a database that most likely contains the appropriate answer. An object and/or a property within the original question can be identified and used to formulate a query, using, for example, system query language (SQL), to search for the answer within the chosen database. The invention makes efficient use of available information, and improves training time and error rate relative to use of single part ANNs.
    • 公开了一种用于自动问题分类和回答的系统和方法。 包括主ANN和辅助ANN的多部分人造神经网络(ANN)根据多个定义的类别之一对接收到的问题进行分类。 从诸如多个人类志愿者的来源接收未标记的数据。 未标记的数据包括可能会询问诸如人形机器人的自主机器的另外的问题,并且用于以无监督的方式训练辅助ANN。 无监督训练可以包括从未标记的数据生成标记数据的多个辅助任务,从而学习底层结构。 辅助ANN训练后,重量被冻结并转移到主ANN。 然后可以使用标记的问题来训练主要的ANN。 要回答的原始问题适用于经过训练的主ANN,该ANN分配一个定义的类别。 分配的类别用于将原始问题映射到最有可能包含适当答案的数据库。 原始问题中的对象和/或属性可以被识别并用于使用例如系统查询语言(SQL)来制定查询来搜索所选择的数据库中的答案。 本发明有效利用可用信息,并且相对于使用单一部件ANN而提高训练时间和错误率。