Incremental computation for structured argumentation over dynamic DeLP knowledge bases

• Published in: Artificial intelligence Vol. 300; p. 103553
• Main Authors: Alfano, Gianvincenzo, Greco, Sergio, Parisi, Francesco, Simari, Gerardo I., Simari, Guillermo R.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2021; Elsevier Science Ltd
• Subjects: Defeasible logic programming; Dynamic DeLP argumentation; Knowledge; Knowledge bases (artificial intelligence); Knowledge representation; Logic programming; Structured argumentation; Structured argumentation; Defeasible logic programming; Dynamic DeLP argumentation
• DOI: 10.1016/j.artint.2021.103553
• ISSN: 0004-3702

Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main output of a dialectical process. Computing such statuses is a costly process, and any update to the knowledge base could potentially have a huge impact if done naively. In this work, we investigate the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations—central to this is the development of structures (e.g. graphs) to keep track of which results can potentially be affected by a given update. We introduce a technique for the incremental computation of the warrant statuses of conclusions in DeLP knowledge bases that evolve due to the application of (sets of) updates. We present the results of a thorough experimental evaluation showing that our incremental approach yields significantly faster running times in practice, as well as overall fewer recomputations, even in the case of sets of updates performed simultaneously.