SouCI: Socially Sustainable Computational Intelligence.

我的研究旨在构建社会可持续的计算智能(SOURCI)。SOURCI结合了一个观点，即人类的学习方式基本上是分层的和生成性的/预测性的，并结合了一个实用的基于模型的分层贝叶斯强化学习模型，部分可观测的马尔可夫决策过程，或POMDP。POMDP被视为在不确定但可合理预测的环境中进行自治代理策略计算的通用模型。此外，通过使这些模型的参数成为模型本身的一部分，代理同时了解世界是如何工作的，以及它的角色是什么。然而，模型参数空间是连续的，这给计算带来了巨大的挑战。我的目标是通过利用人类大脑中情绪处理的洞察力来克服这一挑战。现在人们知道，在一个不确定的，特别是社交的世界里，情绪在帮助人们克服行动带来的计算困难方面发挥着重要作用。情绪被认为是“身体标记”，允许人们类比地将一个领域中使用的技能转移到另一个领域。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POMDP。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，帮助为自主代理和机器人铺平道路，这些自主代理和机器人是由人类和机器人弥漫的社会的真正成员，旨在实现自然资源(环境)、社会(经济)以及人们(社会)的自由、正义和平等的可持续性。我将建立一个名为SOURCI的社会学习系统，旨在通过一系列应用领域实现社会可持续发展，并将展示这些领域如何能够带来经济增长和环境稳定。SOURCI可以分为三个相互关联的组成部分：可学习性、困境解决和伦理/公平。如果代理希望摆脱纯粹代价高昂的理性决策的陷阱，他们必须能够分布式学习共享的行动策略。此外，这些机制必须有助于加强合作，例如为可持续发展所面临的社会困境提供解决办法(例如搭便车)。这样的代理人应该是“可编程的”，在这个意义上，人们可以在代理人使用的高级预测性(生成性)模型中明确地编程衡量公平的程度。这类代理人还被要求学习旨在和平、多样性、包容性、进步和公平的模型，而不是相反的。代理人权衡这些不同因素的程度就是可以在先前的模型规范中使用的“调谐旋钮”，以使代理人的伦理推理围绕文化规范进行。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。

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.