Real-time Heuristic Search for Cooperative and Explainable Autonomous Agents

合作且可解释的自主代理的实时启发式搜索

• 批准号: RGPIN-2019-06132
• 负责人: Bulitko, Vadim
• 金额: $ 2.99万
• 依托单位: University of Alberta
• 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=715945
• 关键词: Real; time; Heuristic; Search; Cooperative

Real-time heuristic search (RTHS) allows Artificial Intelligence (AI) agents to make decisions in real time, with incomplete information. Since 2003 my research group has produced a series of state-of-the-art RTHS algorithms. We accomplished that by introducing several key ideas to the field. However, even contemporary heuristic search methods still face several challenges. First, RTHS algorithms and their parameters are typically manually designed/optimized for each type of search problem. Thus applying RTHS techniques to a new search problem usually requires an RTHS expert, restricting applicability of RTHS. Second, while our recent work showed that RTHS algorithms can sometimes be automatically formed from a set of building blocks, the blocks themselves are manually engineered limiting their variety and injecting human bias. Third, an RTHS agent typically learns only from its own individual experience. Fourth, an agent's reasoning can be difficult to express in a compact human-comprehensible way. This is detrimental for AI agents embedded in human society where the ability to explain one's actions is key to trust and collaboration. My research program will address these shortcomings as follows. We will start by extending our recent work on automated search in the space of RTHS algorithms as well as automated per-problem algorithm selection. We recently used deep learning techniques to map a description of a search problem to the most suitable RTHS algorithm, obtaining promising results. Thus, the short-term objective of this research program is to further investigate cross-domain portability of such mapping and its applicability to broader algorithm spaces. We will address the second shortcoming by adding deep neural networks as a new building block to be incorporated into RTHS. We will evolve the networks in a simulated neuroevolution where survival is linked to search performance. Depending on the evolution setting, RTHS agents may replace some/all of their traditional parts (e.g., lookahead search) with evolved deep networks. As RTHS agents form a population, the ability to communicate and explain their reasoning/knowledge to each other will be an evolutionary adaptation as it would allow them to share learned knowledge among themselves, complementing each agent's individual learning experience. Furthermore, by situating the simulation in a video-game-like environment, we will involve humans in the evolution process and set up a survival structure to encourage RTHS agents to explain their actions to humans as well. Thus, high-performing RTHS agents will not only search well but also be able to explain their search strategy to humans. Finally, we will develop machine-learned detectors to recognize emergence of novel RTHS algorithms automatically. Benefits of this research program include new high performance autonomous agents that can learn collectively and explain their reasoning to humans.

实时启发式搜索（RTHS）允许人工智能（AI）代理在真实的时间内做出决策，信息不完整。自2003年以来，我的研究小组已经产生了一系列最先进的RTHS算法。我们通过向该领域引入几个关键思想来实现这一目标。然而，即使是当代的启发式搜索方法仍然面临着一些挑战。首先，RTHS算法和它们的参数通常针对每种类型的搜索问题手动设计/优化。因此，应用RTHS技术的一个新的搜索问题，通常需要一个RTHS专家，限制了RTHS的适用性。其次，虽然我们最近的工作表明，RTHS算法有时可以从一组构建块自动形成，但这些块本身是手动设计的，限制了它们的多样性并注入了人类偏见。第三，RTHS代理通常只从自己的个人经验中学习。第四，代理的推理可能难以以紧凑的人类可理解的方式表达。这对于嵌入人类社会的人工智能代理来说是有害的，在人类社会中，解释自己行为的能力是信任和协作的关键。 我的研究计划将解决这些缺点如下。我们将首先扩展我们最近在RTHS算法空间中的自动搜索以及自动化每个问题算法选择方面的工作。我们最近使用深度学习技术将搜索问题的描述映射到最合适的RTHS算法，获得了令人鼓舞的结果。因此，本研究计划的短期目标是进一步研究这种映射的跨域可移植性及其对更广泛算法空间的适用性。我们将通过添加深度神经网络作为新的构建模块来解决第二个缺点。我们将在模拟神经进化中进化网络，其中生存与搜索性能相关。根据演进设置，RTHS代理可以替换它们的一些/所有传统部分（例如，前瞻搜索）与进化的深度网络。由于RTHS代理人形成一个群体，相互沟通和解释他们的推理/知识的能力将是一种进化适应，因为它将允许他们在自己之间分享学到的知识，补充每个代理人的个人学习经验。此外，通过将模拟置于类似视频游戏的环境中，我们将使人类参与进化过程，并建立一个生存结构，以鼓励RTHS代理向人类解释他们的行为。因此，高性能的RTHS代理不仅可以很好地搜索，而且还能够向人类解释他们的搜索策略。 最后，我们将开发机器学习检测器来自动识别新RTHS算法的出现。这项研究计划的好处包括新的高性能自主代理，可以集体学习并向人类解释他们的推理。