3 citations · 8 across the 19 of their papers we have counts for
5 papers · 1 filter
A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
Patrick Betz, Nathanael Stelzner, Christian Meilicke +2
In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph…
Fact Probability Vector Based Goal Recognition
Nils Wilken, Lea Cohausz, Christian Bartelt +1
We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial…
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?
Nils Wilken, Lea Cohausz, Christian Bartelt +1
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is im…
Leveraging Planning Landmarks for Hybrid Online Goal Recognition
Nils Wilken, Lea Cohausz, Johannes Schaum +3
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is imp…
xRAI: Explainable Representations through AI
Christiann Bartelt, Sascha Marton, Heiner Stuckenschmidt
We present xRAI an approach for extracting symbolic representations of the mathematical function a neural network was supposed to learn from the trained network. The approach is ba…