SouCI: Socially Sustainable Computational Intelligence.

SouCI：社会可持续计算智能。

• 批准号: RGPIN-2022-03862
• 负责人: Hoey, Jesse
• 金额: $ 2.11万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750200
• 关键词: SouCI; Socially; Sustainable; Computational; Intelligence.

My research aims to build socially sustainable computational intelligence (SouCI). SouCI combines a view that the way humans learn is fundamentally hierarchical and generative/predictive, with a practical model-based hierarchical Bayesian reinforcement learning model, the partially observable Markov decision processes, or POMDP. POMDPs are viewed as general purpose models for autonomous agent policy computation in uncertain, yet reasonably predictable environments. Further, by making the parameters of these models part of the model itself, an agent learns how the world works, and what its role is, at the same time. Model parameter spaces are continuous, however, posing a significant computational challenge. My aim is to overcome this challenge by leveraging insights from emotional processing in the human brain. It is now known that emotions play a significant role in helping people overcome the computational difficulties posed by action in an uncertain, particularly social, world. Emotions are thought to serve as "somatic markers" that allow people to analogically transfer skills used in one domain to another. I plan to use the same types of mechanisms to more efficiently learn these hybrid, emotion-based, POMDPs. I aim to build computational intelligence that mirrors the social and emotional aspects of human intelligence, helping pave the way towards autonomous agents and robots that are genuine members of a society pervaded by humans and robots which aims for sustainability of natural resources (environmental), society (economic), and freedom, justice and equality for people (social). I will build a social learning system called SouCI that aims for social sustainability across a range of application areas, and will show how these can lead to economic growth and environmental stability as well. SouCI may be divided into three interrelated components: learnability, dilemma resolution, and ethics/fairness. Agents must be capable of distributed learning of shared policies of action, should they wish to move away from the trap of pure costly rational decision making. Further, these mechanisms must be such as to enhance cooperation, such as to provide a solution to social dilemmas underlying sustainability (e.g. free-riding). Such agents should be "programmable'' in the sense that one could explicitly program a measure of fairness into the high level predictive (generative) models used by the agent. Such agents are also required to learn models which aim for peace, diversity, inclusion, progress, and equity, rather than the opposites. The degree to which an agent trades-off these different elements is then the "tuning knob'' that can be used in prior model specification to center an agent's ethical reasoning around the cultural norm. All three components have the common goal of motivating itself and its group (including humans) to be more cooperative.

我的研究旨在建立社会可持续的计算智能（SouCI）。SouCI将人类学习方式从根本上讲是分层和生成/预测的观点与基于实际模型的分层贝叶斯强化学习模型，部分可观察马尔可夫决策过程或POMDP相结合。POMDPs被视为通用模型的自主代理政策计算在不确定的，但合理的可预测的环境。此外，通过使这些模型的参数成为模型本身的一部分，智能体可以同时学习世界是如何运作的，以及它的角色是什么。然而，模型参数空间是连续的，这带来了重大的计算挑战。我的目标是通过利用人类大脑中情绪处理的见解来克服这一挑战。现在我们知道，情绪在帮助人们克服在不确定的，特别是社会世界中行动所带来的计算困难方面发挥着重要作用。情绪被认为是“躯体标记”，允许人们将一个领域中使用的技能类比地转移到另一个领域。我计划使用相同类型的机制来更有效地学习这些混合的、基于情感的POMDPs。我的目标是建立反映人类智能的社会和情感方面的计算智能，帮助为自主代理人和机器人铺平道路，这些机器人是人类和机器人遍布的社会的真正成员，旨在实现自然资源（环境），社会（经济）的可持续性，以及人们（社会）的自由，正义和平等。我将建立一个名为SouCI的社会学习系统，旨在实现一系列应用领域的社会可持续性，并将展示这些系统如何促进经济增长和环境稳定。SouCI可以分为三个相互关联的组成部分：可学习性，困境解决和道德/公平。代理必须能够分布式学习共享的行动政策，如果他们希望摆脱纯粹昂贵的理性决策的陷阱。此外，这些机制必须能够加强合作，例如为可持续性所面临的社会困境（例如搭便车）提供解决办法。这样的代理应该是“可编程的”，在这个意义上，一个人可以明确地编程到代理使用的高级预测（生成）模型的公平性的措施。这些行为者还需要学习以和平、多样性、包容、进步和公平为目标的模式，而不是相反的模式。代理商权衡这些不同元素的程度是“调整旋钮”，可以用于先前的模型规范，以使代理商的道德推理围绕文化规范。 所有这三个组成部分都有一个共同的目标，那就是激励自己和它的团队（包括人类）更加合作。