Research & Decision Science
Research built into the product
Forecasting, optimization and applied AI for real decisions under real constraints.
- indexed research outputs
- 1,181indexed research outputs
- combined citations
- 53,485combined citations
- Clarivate Highly Cited Papers in forecasting
- Top 1%Clarivate Highly Cited Papers in forecasting
Scientific foundations
From research result to product behavior
Probabilistic forecasts, coherent hierarchies, constraint-aware recommendations and governed human intervention.
Research → product
| Research area | Enterprise problem | RabbitHawk capability |
|---|---|---|
| Hierarchical forecasting | Local forecasts do not aggregate coherently across the enterprise | Reconciled forecasts across every level |
| Probabilistic forecasting | Point forecasts hide risk | Full predictive distributions, intervals and scenario ranges |
| Optimization | Forecasts do not tell teams what to do | Constraint-aware recommendations |
| Agentic AI | Context is trapped in emails and notes | Adaptive elicitation, semantic routing and governed context workflows |
| Human-in-the-loop AI | Automation without trust creates resistance | Belief review, governed publication and intervention lineage |
| Alignment science | Teams optimize against conflicting goals | Goals & Guardrails within Forecasting & Optimization |
Hierarchical forecasting
Local forecasts do not aggregate coherently across the enterprise
Reconciled forecasts across every level
Probabilistic forecasting
Point forecasts hide risk
Full predictive distributions, intervals and scenario ranges
Optimization
Forecasts do not tell teams what to do
Constraint-aware recommendations
Agentic AI
Context is trapped in emails and notes
Adaptive elicitation, semantic routing and governed context workflows
Human-in-the-loop AI
Automation without trust creates resistance
Belief review, governed publication and intervention lineage
Alignment science
Teams optimize against conflicting goals
Goals & Guardrails within Forecasting & Optimization
Research database
Search the indexed record by title, author, venue, category or year. Records include peer-reviewed work, preprints, books, chapters and other scholarly outputs.
1,181 results · page 30 of 99
2020
Nested ECBS for bounded-suboptimal multi-agent path finding
SH Chan, J Li, D Harabor, PJ Stuckey, G Gange, L Cohen, S Koenig
Proceedings of the IJCAI Workshop on Multi-Agent Path Finding. Yokohama, 2020
Multi-Agent Systems2 citationsAlgorithm selection for dynamic symbolic execution: A preliminary study
R Amadini, G Gange, P Schachte, H Søndergaard, PJ Stuckey
International Symposium on Logic-Based Program Synthesis and Transformation …, 2020
Research1 citationsAggregation and Garbage Collection for Online Optimization
A Ek, M Garcia de la Banda, A Schutt, PJ Stuckey, G Tack
International Conference on Principles and Practice of Constraint …, 2020
Optimization1 citationsDiscriminative, generative and self-supervised approaches for target-agnostic learning
Y Jin, W Buntine, F Petitjean, GI Webb
arXiv preprint arXiv:2011.06428, 2020
Research1 citationsLeveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Y Jin, H Zhao, M Liu, Y Zhu, L Du, L Gao, H Zhang, Y Li
arXiv preprint arXiv:2002.09145, 2020
Research1 citationsSelf-organising Neural Network Hierarchy
S Borgohain, G Kowadlo, D Rawlinson, C Bergmeir, K Loo, H Rangarajan, ...
Australasian Joint Conference on Artificial Intelligence, 359-370, 2020
Machine LearningRecent Developments in the Design and Implementation of Programming Languages
FS de Boer, J Mauro
Schloss Dagstuhl-Leibniz-Zentrum für Informatik GmbH, 2020
ResearchRandom errors are not politically neutral.
ML Blom, A Conway, PJ Stuckey, V Teague, D Vukcevic
CoRR, 2020
Research- Machine Learning
2019
Searching with consistent prioritization for multi-agent path finding
H Ma, D Harabor, PJ Stuckey, J Li, S Koenig
Proceedings of the AAAI conference on artificial intelligence 33 (01), 7643-7650, 2019
Optimization350 citationsSales demand forecast in e-commerce using a long short-term memory neural network methodology
K Bandara, P Shi, C Bergmeir, H Hewamalage, Q Tran, B Seaman
International conference on neural information processing, 462-474, 2019
Machine Learning286 citationsShort-term planning for open pit mines: a review
M Blom, AR Pearce, PJ Stuckey
International Journal of Mining, Reclamation and Environment 33 (5), 318-339, 2019
Optimization162 citations
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