Using Abstraction in Reasoning about Autonomous Agents and Multiagent Systems

在自治代理和多代理系统的推理中使用抽象

• 批准号: RGPIN-2022-04565
• 负责人: Lesperance, Yves
• 金额: $ 2.11万
• 依托单位: York University
• 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=752431
• 关键词: Using; Abstraction; Reasoning; about; Autonomous

When developing autonomous agents that perform tasks in complex dynamic environments, the use of abstraction is crucial in planning agent action, in explaining the agent's behaviour, and in doing reinforcement learning. We can use a simplified abstract model to generate high-level solutions efficiently, and later refine these using the detailed concrete model. The abstract model may be expressed in terms that humans understand, while the concrete model can be used by the machine. More generally, we may have a multi-tier representation where various models are used to perform reasoning at different levels of detail and address different kinds of contingencies. In recent work with Banihashemi and De Giacomo, I have developed a formal account of agent abstraction in the situation calculus, a well known predicate logic framework for reasoning about action. We assume that we have a high--level specification and a low--level specification of the agent, both represented as action theories in the situation calculus. A refinement mapping specifies how each high--level action is implemented by a low--level ConGolog program and how each high--level predicate can be translated into a low--level formula. We define notions of sound/complete abstractions between such action theories. We showed that sound/complete abstractions have many useful properties that ensure that we can reason about the agent's actions (e.g., synthesize plans) at the abstract level, and then refine the solutions obtained at the low level. The framework can also be used to generate high--level explanations of low--level behavior. In this project, my students and I will extend this work and apply it to new problems. First, we will examine how to generalize our account to apply to nondeterministic domains, where actions have many possible outcomes that are not under the agent's control. Second, we will study how abstraction can be exploited to perform explainable planning, where we bridge the gap between the human user's model and the system's model. Third, we will examine how to accommodate temporally extended goals and abstract plans in "practical reasoning" where an agent progressively refines/revises her intentions over time, while keeping them consistent; the agent should not have to consider fully detailed plans and should be able to exploit knowledge about how goals/plans interact. Fourth, we will study how abstraction can be used in reasoning about other agents and how they can help/interfere with the accomplishment of the one's goals; this should yield a form of decentralized multi-agent epistemic planning, where each agent generates her own plan, delegating subgoals to other agents, and knows enough about the other agents' abilities, intentions, and willingness to cooperate to be confident that her goals will be achieved. Finally, we will look at how to synthesize abstractions that are useful for a given application/purpose.

当开发在复杂的动态环境中执行任务的自主代理时，抽象的使用在规划代理动作、解释代理的行为和进行强化学习中至关重要。我们可以使用简化的抽象模型来有效地生成高级解决方案，然后使用详细的具体模型来细化这些解决方案。抽象模型可以用人类理解的术语来表达，而具体模型可以由机器使用。更一般地说，我们可能有一个多层表示，其中各种模型用于在不同的细节级别执行推理，并解决不同类型的突发事件。在最近的工作Banihashemi和德贾科莫，我已经开发了一个正式的帐户代理抽象的情况演算，一个著名的谓词逻辑框架推理的行动。我们假设，我们有一个高层次的规范和低层次的规范的代理，都表示为行动理论的情况演算。细化映射指定每个高级操作如何由低级ConGolog程序实现，以及每个高级谓词如何转换为低级公式。我们定义的声音/完整的抽象之间的行动理论的概念。我们发现，健全/完整的抽象有许多有用的属性，确保我们可以推理代理的行为（例如，综合计划），然后细化在低级别获得的解决方案。该框架也可以用来生成低层次行为的高层次解释。在这个项目中，我的学生和我将扩展这项工作，并将其应用到新的问题。首先，我们将研究如何推广我们的帐户适用于不确定性域，其中的行动有许多可能的结果，不受代理的控制。其次，我们将研究如何利用抽象来执行可解释的规划，在那里我们弥合人类用户模型和系统模型之间的差距。第三，我们将研究如何适应时间上扩展的目标和抽象的计划，在“实践推理”中，代理人随着时间的推移逐步完善/修改她的意图，同时保持一致;代理人不应该考虑完全详细的计划，应该能够利用目标/计划如何相互作用的知识。第四，我们将研究抽象如何用于对其他代理的推理，以及它们如何帮助/干扰一个人的目标的实现;这应该产生一种分散的多代理认知规划的形式，其中每个代理生成她自己的计划，将子目标委托给其他代理，并且足够了解其他代理的能力，意图，并愿意合作，相信她的目标会实现。最后，我们将研究如何合成对给定应用程序/目的有用的抽象。