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

这个项目的主要目标是丰富和扩展的框架，合理的推理和信念修订的符号人工智能集成在一个深层次的方法论基础上的两种技术，从概率推理，这是基本的，允许本地推理的小subsignatures通过条件：语法分裂和运动学。双分裂根据签名的子集划分模型的语义空间;运动学允许根据（排他性）情况进一步划分。通过这种方式，语法分裂和运动学原理沿着沿着两个维度构造推理和修正任务，并支持它们在局部子空间上的更有效的解决方案。局部推理和局部修正（分别在语义子空间上）信念基础的子集）是这个项目的关键概念。一个主要的挑战是（重新）构建一个整体的解决方案的归纳推理。从局部解对整个语义空间进行修正。该项目的结果将具有深远的实际和理论影响，由解决归纳推理和迭代修订的两个分裂维度的基准问题库支持，并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，用工作台和演示系统进行评估。新的技术远远超出了目前的最先进的状态，也可以管理先进的推理和修改方法的新公理将被开发。这将是可能的，通过建立一个连贯和统一的框架，推理和修订，这是完全基于认知状态和条件。为了表示认知状态，我们依赖于两个最广泛使用的语义框架的非单调推理和信念修正，即总前序（TPO）和有序条件函数（OCF）。我们的目标是最大限度地利用这两种语义之间的相互关系，同时利用更强大的结构的TPO方法的OCF。此外，我们利用的C-表示和C-修订的OCF已受到启发的概率推理/修订，连同策略，管理推理/修订任务中的条件句的影响，在一个连贯和原则的方式。他们的就业合并到全球解决方案的本地解决方案提供了一个全新的视角（条件）合并。C-表示/C-修改的合适策略的公理化描述不仅对于定义合适的操作符具有实际意义。这也传达了深刻的方法论见解推理和修订的条件，由于管理的相互作用下的归纳推理/信念修订的条件信念。这样，我们的方法的基本思想和主要结果将可移植到其他方法的归纳推理和修订。

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.