Plausible Reasoning and Revision in AI Along Two Dimensions: Syntax Splitting and Kinematics Principles

人工智能中两个维度的合理推理和修正：语法分割和运动学原理

• 批准号: 512363537
• 负责人: Professor Dr. Christoph Beierle
• 金额: --
• 依托单位: Informatik I - Lehrstuhl Logik in der Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/512363537?language=en
• 关键词: Plausible; Reasoning; Revision; AI; Along

The main goal of this project is to enrich and extend the frameworks of plausible reasoning and belief revision in symbolic Artificial Intelligence by integrating on a deep methodological base two techniques from probabilistic reasoning which are fundamental to allow for local reasoning on small subsignatures via conditionals: syntax splitting and kinematics. Syntax splitting divides the semantic space of models according to subsets of the signature; kinematics allows for further dividing it according to (exclusive) cases. In this way, syntax splitting and kinematics principles structure reasoning and revision tasks along two dimensions, and support their more efficient solution on local subspaces. Local reasoning and local revision (on semantic subspaces resp. on subsets of the belief bases) are key concepts of this project. A major challenge is to (re)construct a global solution of the inductive reasoning resp. revision task on the whole semantic space from the local solutions. The results of the project will have far-reaching both practical and theoretical impacts, supported by a repository of benchmark problems addressing the two splitting dimensions of inductive reasoning and iterated revision and evaluated with a workbench and demonstrator system, by providing more efficient algorithms and implementations, and by dealing with the processing of sets of conditionals for inductive reasoning and belief revision. Novel techniques far beyond the current state of the art and also novel axioms that may govern advanced reasoning and revision approaches will be developed. This will be possible by setting up a coherent and unified framework for reasoning and revision which is based thoroughly on epistemic states and conditionals. For representing epistemic states, we rely on two of the most broadly used semantic frameworks for nonmonotonic inference and belief revision, namely total preorders (TPO) and ordinal conditional functions (OCF). We aim at taking maximal benefit from the interrelationships between both semantics while exploiting the stronger structure of OCFs for TPO methods. Moreover, we make use of c-representations and c-revisions for OCFs which have been inspired by probabilistic reasoning/revision, together with strategies which govern the impacts of conditionals in reasoning/revision tasks in a coherent and principled way. Their employment for merging local solutions into global solutions offers a completely novel perspective on (conditional) merging. The axiomatic description of suitable strategies for c-representations/c-revisions is not only of practical relevance to define suitable operators. This also conveys deep methodological insights into reasoning with and revision by conditionals due to governing the interactions of sets of conditional beliefs under inductive reasoning/belief revision. In this way, basic ideas and main results of our approach will be transferrable to other approaches to inductive reasoning and revision.

该项目的主要目标是丰富和扩展符号人工智能中可信推理和信念修正的框架，方法是在深度方法论基础上集成概率推理的两种技术，这两种技术是通过条件对小子签名进行局部推理的基础：语法分裂和运动学。语法分割是根据签名的子集划分模型的语义空间；运动学允许进一步划分它根据（独家）的情况。通过这种方式，语法分裂和运动学原理沿着两个维度构建推理和修正任务，并支持它们在局部子空间上更有效地求解。语义子空间的局部推理与局部修正。在信念基础的子集上)是这个项目的关键概念。一个主要的挑战是（重新）构建归纳推理问题的全局解。修正任务从整体语义空间的局部解出发。该项目的结果将具有深远的实践和理论影响，通过提供更有效的算法和实现，以及处理归纳推理和信念修订的条件集的处理，通过解决归纳推理和迭代修订的两个分裂维度的基准问题存储库，并通过工作台和演示系统进行评估，支持该项目的结果。将开发出远远超出当前技术水平的新技术，以及可能管理高级推理和修正方法的新公理。这将通过建立一个完全基于认知状态和条件的连贯和统一的推理和修订框架来实现。为了表示认知状态，我们依赖于两种最广泛使用的非单调推理和信念修正语义框架，即总预数（TPO）和有序条件函数（OCF）。我们的目标是从两个语义之间的相互关系中获得最大的利益，同时利用ocf对TPO方法的更强结构。此外，我们还利用c-表示和c-修正的ocf，这些ocf受到概率推理/修正的启发，以及以连贯和有原则的方式管理推理/修正任务中条件影响的策略。它们用于将本地解决方案合并到全局解决方案中，为（有条件的）合并提供了全新的视角。c-表示/c-修正的合适策略的公理化描述不仅与定义合适的操作符具有实际意义。由于在归纳推理/信念修正下控制条件信念集的相互作用，这也传达了对条件推理和条件修正的深刻方法论见解。这样，我们的方法的基本思想和主要结果将被转移到其他方法的归纳推理和修订。