G
Graeme Gange
Researcher at Monash University
Publications - 88
Citations - 914
Graeme Gange is an academic researcher from Monash University. The author has contributed to research in topics: Constraint programming & Constraint (information theory). The author has an hindex of 17, co-authored 87 publications receiving 716 citations. Previous affiliations of Graeme Gange include University of Melbourne & Monash University, Clayton campus.
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Proceedings Article
Lazy CBS: Implicit Conflict-Based Search Using Lazy Clause Generation
TL;DR: Lazy CBS is presented, a new approach to multi-agent pathfinding which replaces the high-level solver of CBS with a lazily constructed constraint programming model with nogoods, and can significantly improve on the state-of-the-art for optimal MAPF problems under the sumof-costs metric.
Proceedings ArticleDOI
SLA-Based Resource Scheduling for Big Data Analytics as a Service in Cloud Computing Environments
TL;DR: This research proposes an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution.
Proceedings Article
New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding.
TL;DR: This work considers two new classes of pairwise path symmetries which appear in the context of Multi-Agent Path Finding and proposes new reasoning techniques that detect each class of symmetry and resolve them by introducing specialized constraints.
Journal ArticleDOI
Pairwise symmetry reasoning for multi-agent path finding search
TL;DR: This work proposes a variety of reasoning techniques that detect the symmetries efficiently as they arise and resolve them by using specialized constraints to eliminate all permutations of pairwise colliding paths in a single branching step, and implements these ideas in the context of a leading optimal MAPF algorithm CBS.
Journal ArticleDOI
MDD propagators with explanation
TL;DR: An incremental propagation algorithm for MDDs is introduced, and several methods for incorporating explanations with MDD-based propagators are explored, demonstrating that these techniques can provide significantly improved performance when solving a variety of problems.