Knowledge Graphs

IARPA HAYSTACK - Anamoly Detection
Our team at LabV2(ASU) [collaboration with Leidos Inc.] works on modeling agent movements in real-world maps. We use knowledge graphs to model the maps in combination with symbolic rule learning and heuristic based graph traversal algorithms to model and learn agent trajectories(both anamolous and normal).
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IARPA HAYSTACK - Anamoly Detection
Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing
We present a framework for constrained optimization of agent trajectory in symbolic-knowledge infused graphs using heuristic based graph traversal algorithms – A* search.
[Accepted to International Conference on Logic Programming (ICLP 2024)]
Divyagna Bavikadi, Dyuman Aditya, Devendra R. Parkar, Paulo Shakarian, Graham Mueller, Chad Parvis, Gerardo I. Simari
Project DOI
Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing