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    • 2. 发明申请
    • REAL TIME CONTEXT LEARNING BY SOFTWARE AGENTS
    • 软件代理的实时语境学习
    • US20070260567A1
    • 2007-11-08
    • US10885495
    • 2004-07-06
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • G06F17/00
    • A63F13/67A63F13/10A63F2300/60A63F2300/6027G06N99/005
    • Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.
    • 在模拟中为软件代理提供动态学习。 具有学习者的软件代理能够从示例中学习。 当非玩家角色查询学习者时,它可以提供类似于玩家角色的下一个动作。 游戏设计师提供程序代码,编译时步骤确定一组原始特征。 代码可能会识别一个函数(如计算距离)。 在编译时步骤中,根据脚本语言确定这些原始特征,因此设计人员可以指定哪些代码应该被引用。 响应于原始特征的一组派生特征可能相对简单,更复杂或响应于学习者而确定。 这些原始和派生特征的集合形成了学习者的上下文。 学习者可以对(更基础的)学习者,状态机的结果,计算的导出特征或者原始特征进行响应。 学习者包括机器学习技术。
    • 5. 发明授权
    • Query controlled behavior models as components of intelligent agents
    • 查询控制行为模型作为智能代理的组成部分
    • US07636701B2
    • 2009-12-22
    • US11929170
    • 2007-10-30
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • G06N5/00
    • A63F13/67A63F13/10A63F2300/60A63F2300/6027G06N99/005
    • Providing dynamic learning for software agents in a simulation is described. The software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to a player character. A game designer provides program code, from which compile-time steps determine a set of raw features. The code may identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, may be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.
    • 描述了在仿真中为软件代理提供动态学习。 具有学习者的软件代理能够从示例中学习。 当非玩家角色询问学习者时,它可以提供类似于玩家角色的下一个动作。 游戏设计师提供程序代码,编译时步骤确定一组原始特征。 代码可以识别功能(如计算距离)。 在编译时步骤中,根据脚本语言确定这些原始特征,因此设计人员可以指定哪些代码应该被引用。 响应于原始特征的一组派生特征可能相对简单,更复杂或响应于学习者而确定。 这些原始和派生特征的集合形成了学习者的上下文。 学习者可以对(更基础的)学习者,状态机的结果,计算的导出特征或者原始特征进行响应。 学习者包括机器学习技术。
    • 6. 发明申请
    • Real Time Context Learning by Software Agents
    • 软件代理的实时上下文学习
    • US20080097948A1
    • 2008-04-24
    • US11929170
    • 2007-10-30
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • G06N5/00
    • A63F13/67A63F13/10A63F2300/60A63F2300/6027G06N99/005
    • Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.
    • 在模拟中为软件代理提供动态学习。 具有学习者的软件代理能够从示例中学习。 当非玩家角色查询学习者时,它可以提供类似于玩家角色的下一个动作。 游戏设计师提供程序代码,编译时步骤确定一组原始特征。 代码可能会识别一个函数(如计算距离)。 在编译时步骤中,根据脚本语言确定这些原始特征,因此设计人员可以指定哪些代码应该被引用。 响应于原始特征的一组派生特征可能相对简单,更复杂或响应于学习者而确定。 这些原始和派生特征的集合形成了学习者的上下文。 学习者可以对(更基础的)学习者,状态机的结果,计算的导出特征或者原始特征进行响应。 学习者包括机器学习技术。
    • 7. 发明授权
    • Real time context learning by software agents
    • 软件代理实时上下文学习
    • US07296007B1
    • 2007-11-13
    • US10885495
    • 2004-07-06
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • John FungeRon MusickDaniel DobsonNigel DuffyMichael McNallyXiaoyuan TuIan WrightWei YenBrian Cabral
    • G06F17/00G06N5/00
    • A63F13/67A63F13/10A63F2300/60A63F2300/6027G06N99/005
    • Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.
    • 在模拟中为软件代理提供动态学习。 具有学习者的软件代理能够从示例中学习。 当非玩家角色查询学习者时,它可以提供类似于玩家角色的下一个动作。 游戏设计师提供程序代码,编译时步骤确定一组原始特征。 代码可能会识别一个函数(如计算距离)。 在编译时步骤中,根据脚本语言确定这些原始特征,因此设计人员可以指定哪些代码应该被引用。 响应于原始特征的一组派生特征可能相对简单,更复杂或响应于学习者而确定。 这些原始和派生特征的集合形成了学习者的上下文。 学习者可以对(更基础的)学习者,状态机的结果,计算的导出特征或者原始特征进行响应。 学习者包括机器学习技术。