Development of a Bidirectional Associative Memory for Supervised, Unsupervised and Reinforcement Learning

用于监督、无监督和强化学习的双向联想记忆的开发

• 批准号: RGPIN-2019-04097
• 负责人: Chartier, Sylvain
• 金额: $ 2.4万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714630
• 关键词: Development; Bidirectional; Associative; Memory; Supervised

Development of a Bidirectional Associative Memory for Supervised, Unsupervised and Reinforcement Learning. Although it is something most of us do every day without effort, memorizing is in fact an incredibly complex task. For instance, the simple act of storing and retrieving an object is something we have not yet managed to program a machine to do with anything approaching human efficiency and robustness. My research aims to better understand how human cognitive system accomplishes the complex task of create (and enhance) representation from patterns as well as recognize, identify, categorize and classify them. To understand how the human cognitive system works, we need to develop formal models. Nowadays, no formal models can take into account this variety of behavior without scarifying simplicity and self-consistency. In this research, we will use artificial neural networks that are parallel and where information is distributed among the units. Since the brain uses similar computing units and mechanisms for different tasks, this approach uses a bidirectional associative memory (BAM) model as building blocks for a general cognitive architecture. More precisely, this research program focuses on developing a general BAM that can encompass supervised, unsupervised and reinforcement learning. Focus is given on interesting nonlinear dynamics system perspective where time and change are the key variables. Within that perspective memory are represented as invariant or aperiodic states. Moreover, we want to build a better grounding into neuroscience by extending the BAM to handle spiking representation and implement the model into virtual and physical robots. Therefore, it is hoped that by developing such BAM we will have a better understanding on how the brain and mind work, and, how this understanding can be translated into artificial general intelligence.

开发用于监督、无监督和强化学习的双向联想记忆。 虽然我们大多数人每天都在做一些不费力的事情，但记忆实际上是一项非常复杂的任务。例如，存储和检索对象的简单行为是我们还没有设法编程机器来做任何接近人类效率和鲁棒性的事情。 我的研究旨在更好地理解人类认知系统如何完成从模式中创建（和增强）表示的复杂任务，以及识别，识别，分类和分类它们。为了理解人类认知系统是如何工作的，我们需要开发形式化的模型。如今，没有一个正式的模型能够在不牺牲简单性和自洽性的情况下考虑到这种行为的多样性。在这项研究中，我们将使用并行的人工神经网络，其中信息分布在单元之间。由于大脑使用类似的计算单元和机制来执行不同的任务，因此这种方法使用双向联想记忆（BAM）模型作为一般认知架构的构建块。更确切地说，该研究计划的重点是开发一个通用的BAM，可以包括监督，无监督和强化学习。重点是有趣的非线性动力学系统的角度来看，时间和变化是关键变量。在这种观点中，记忆被表示为不变或非周期性的状态。此外，我们希望通过扩展BAM来处理尖峰表示并将模型实现为虚拟和物理机器人，从而为神经科学奠定更好的基础。 因此，希望通过开发这样的BAM，我们将更好地了解大脑和心灵如何工作，以及如何将这种理解转化为人工智能。