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量子马尔科夫毯子用于超学习的经典推理悖论,具有低于最佳的自由能量
Kevin B Clark1,2,3,4,5,6,7,8,9,10,11
1Cures Within Reach, Chicago, IL, USA kbclarkphd@yahoo.comwww.linkedin.com/pub/kevin-clark/58/67/19ahttps://access-ci.org/.
量子主动贝叶斯推理和量子马尔科夫毯子模型复杂的决策悖论. 这种方法使用量子原理解释非理性选择,并对可验证结果产生现实世界的影响.
科学领域:
- 量子力学就是量子力学.
- 认知科学是一种认知科学.
- 计算建模计算建模
背景情况:
- 经典的推理悖论对基于代理的建模提出了挑战.
- 在复杂的任务特定环境中理解决策需要先进的理论框架.
研究的目的:
- 引入量子主动贝叶斯推理和量子马尔科夫毯子,用于强大的建模.
- 在非现实主义的认知完整性制度中解释meta-learned非理性决策.
主要方法:
- 使用量子马尔科夫毯子来确保决策符合可解释的多元体.
- 为meta-learned过程优化自由能量.
- 基于代理的模型与特定任务的环境进行接口.
主要成果:
- 证明了经典推理悖论的强大建模和模拟.
- 展示了量子马尔科夫毯子如何促进适应非理性决策以获得最佳的自由能量.
- 识别了可接受的不兼容的观测和时间贝尔不平等的违规行为,作为可验证的现实世界结果.
结论:
- 量子主动贝叶斯推理和量子马尔科夫毯子为复杂的认知建模提供了强大的框架.
- 该方法提供可验证的现实世界结果,包括时间贝尔不等式违规.
- 这项研究将量子理论和认知科学与先进的人工智能和基于代理的系统联系起来.
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