Advanced Techniques for Action Model Solicitation, Verification, and Induction

行动模型征求、验证和归纳的先进技术

• 批准号: RGPIN-2020-05501
• 负责人: Muise, Christian
• 金额: $ 2.48万
• 依托单位: Queen's University
• 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=717692
• 关键词: Advanced; Techniques; Action; Model; Solicitation

Autonomous systems are becoming a vital part of everyday life. Systems that can reason about the world and take goal-directed actions are at the forefront of sophisticated autonomous behaviour, and constructing them remains a challenge. The sub-field of Artificial Intelligence known as Automated Planning (AP) focuses on how to build autonomous systems that interact with environments (both virtual and physical). AP does so by synthesizing plans or policies for an agent to follow, given a model of the environment. AP research has made tremendous strides in recent decades, leading to numerous industry applications including robotics, dialogue generation, and business process automation. This proposal aims to directly address some of the greatest barriers to deploying AP solutions. A key bottleneck for using AP technology is the task of model acquisition: the process of acquiring specifications or models to describe the autonomous system behaviour. The proposed research directly focuses on this crucial and understudied step. In particular, three complementary threads of research will address various aspects of model acquisition -- from manual model solicitation to fully autonomous model induction. The first thread will improve the capability of manual model specification by applying modern AI reasoning techniques to the process. A suite of advanced analysis and verification techniques, such as automated case-based testing of action reachability or redundancy, will identify and highlight model inconsistencies and insufficiencies. The second thread of research will unify model representations by introducing a common logic-based specification. The final thread will focus on model induction: semi- or fully-autonomous inference of AP models in a data-driven fashion. Using a combination of modern machine learning techniques with innate priors informed by AP theory, this line of work can greatly reduce the burden on practitioners synthesizing autonomous systems. Synergy between these three threads will lead to a substantial improvement in the process of model acquisition. All aspects of the work reflect an integrated approach that will form a single framework for model acquisition. This will be made publicly available to both the research community focused on AP techniques, as well as practitioners that deploy AP solutions in industry today. Improved model acquisition will have a direct and substantial impact on the business areas of dialogue agent design and business process automation, both of which depend on action model acquisition. The training of highly qualified personnel for this research will provide them with the opportunity to develop skills in many fundamental and emerging areas of Artificial Intelligence. I expect that two PhD students, two Master's students, and four undergraduate students will acquire training through this program; preparing them for the high-demand area of AI in either academia or industry.

自主系统正在成为日常生活的重要组成部分。能够对世界进行推理并采取目标导向行动的系统处于复杂自主行为的前沿，构建它们仍然是一个挑战。人工智能的子领域被称为自动规划（AP），重点是如何构建与环境（虚拟和物理）交互的自主系统。在给定环境模型的情况下，AP通过综合代理要遵循的计划或策略来实现这一点。近几十年来，AP研究取得了巨大的进步，导致了许多行业应用，包括机器人技术、对话生成和业务流程自动化。本提案旨在直接解决部署AP解决方案的一些最大障碍。