An Implementation of Splitting for Dung Style Argumentation Frameworks

Wong, Renata
Abstract in English: 
Argumentation and reasoning have been an area of research in such disciplines as philosophy, logic and arti ficial intelligence for quite some time now. In the area of AI, knowledge needed for reasoning can be represented using various kinds of representation systems. The natural problem posed by this fact is that of possible incompatibility between heterogeneous systems as far as communication between them is concerned. This imposes a limitation on the possibility of extending smaller knowledge bases to larger ones. In order to facilitate a common platform for exchange across the systems unifi ed formalisms for the di fferent approaches to knowledge representation are required. This was the motivation for Dung [11] to propose in his 1995 paper an approach that later came to be known as an abstract argumentation framework. Roughly speaking, Dung's arguments are abstract entities which are related to each other by the means of conflicts between them. An intuitive graphical representation of Dung style framework is a graph whose nodes stand for arguments and whose edges stand for conflicts. A framework postulated this way is on one hand too general to be used on its own, but on the other hand it is general enough as to allow for varied extensions increasing its expressiveness, which indeed have been proposed. They include value-based argumentation frameworks by Bench-Capon et al. [6], preference-based argumentation frameworks by Amgoud and Cayrol [1] and bipolar argumentation frameworks by Brewka and Woltran [7], to name a few. The present thesis is concerned with yet another variation of Dung's framework: the concept of splitting. It was developed by Baumann [4] with one of the underlying purposes being that the computation time in frameworks which have been split into two parts and then computed separately may show some improvement in comparison to their variant without splitting. It was one of the main tasks of my work to develop an efficient algorithm for the splitting operation based on the theoretical framework given in [4]. On the other hand I hoped to confi rm the expectation that splitting can indeed make a computation perform better.
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