Intentional Forgetting in Distributed Artificial Intelligence

Abstract In times of Big Data and Industry 4.0, organizational information as well as knowledge availability and quantity are driving complex decision-making tasks. Especially for AI systems, increasing knowledge-bases for elaborate computations lead to a lower efficiency of their inference mechanis...
Ausführliche Beschreibung

Abstract In times of Big Data and Industry 4.0, organizational information as well as knowledge availability and quantity are driving complex decision-making tasks. Especially for AI systems, increasing knowledge-bases for elaborate computations lead to a lower efficiency of their inference mechanisms. In contrast to AI, bounded cognitive capacity is a well-known problem in psychology. When humans receive too much information, a state of information overload emerges. In order to cope with limited capacity and prevent information overload, humans adapt their knowledge and delete, override, suppress, or sort out outdated information, i.e., they forget. By transferring theories from human cognition to multiagent systems, the AdaptPRO project adopts intentional forgetting as a strategy for coping with information overload in both human and multiagent teams. This article gives an overview of an interdisciplinary research project with a strong focus on knowledge distributions and knowledge dynamics from a distributed AI perspective. Its core contribution is a formal model for distributing and adapting (meta-) knowledge by intentional forgetting to enable efficient and resilient teamwork. Ausführliche Beschreibung