Research & Decision Science
Where forecasting research becomes operational decision infrastructure
RabbitHawk is built on peer-reviewed work in probabilistic forecasting, hierarchical reconciliation, optimization and applied AI, translated into systems that improve real decisions under real constraints.
- publications
- 1,181publications
- citations
- 53,485citations
- highly cited papers
- Top 1%highly cited papers
Scientific foundations
Methods proven in the literature, applied in production
Our team’s research is recognized at the field’s leading venues and used at scale by major organizations. We bring that rigor to enterprise planning, but judge every method by whether it improves decisions in practical settings.
Research → product
| Research area | Enterprise problem | RabbitHawk capability |
|---|---|---|
| Hierarchical forecasting | Local plans do not add up to executive targets | 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 | Context ingestion and decision agents |
| Human-in-the-loop AI | Automation without trust creates resistance | Approval, override and audit trails |
| Alignment science | Teams optimize against conflicting goals | 90North alignment and goal clarity |
Hierarchical forecasting
Local plans do not add up to executive targets
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
Context ingestion and decision agents
Human-in-the-loop AI
Automation without trust creates resistance
Approval, override and audit trails
Alignment science
Teams optimize against conflicting goals
90North alignment and goal clarity
Selected publications
The full peer-reviewed database: search by title, author, venue, category or year.
1,181 results · page 87 of 99
1998
Optimizing compilation of CLP (ℛ)
AD Kelly, K Marriott, A MacDonald, PJ Stuckey, R Yap
Research13 citationsA practical object‐oriented analysis engine for CLP
AD Kelly, K Marriott, H Søndergaard, PJ Stuckey
Research10 citationsAnalysing Rock Samples for the Mars Lander.
JJ Oliver, T Roush, P Gazis, WL Buntine, RA Baxter, SR Waterhouse
KDD, 299-303, 1998
Research10 citationsAn efficient heuristic-based evolutionary algorithm for solving constraint satisfaction problems
V Tam, P Stuckey
Proceedings. IEEE International Joint Symposia on Intelligence and Systems …, 1998
Machine Learning7 citationsSemantics for using stochastic constraint solvers in constraint logic programming
P Stuckey, V Tam
Journal of Functional and Logic programming 2, 1998
Machine Learning7 citations- Machine Learning6 citations
Improving GENET and EGENET by new variable ordering strategies
V Tam, P Stuckey
International Conference on Computational Intelligence and Multimedia …, 1998
Research6 citationsWill domain-specific code synthesis become a silver bullet?
W Buntine, P Norvig, J Van Baalen, D Spiegelhalter, A Thomas
IEEE Intelligent Systems and their Applications 13 (2), 9-15, 1998
Machine Learning6 citationsGlobal variables in HAL, a logic implementation
B Demoen, M García de la Banda, K Mariott, P Schachte, P Stuckey
CW Reports, 10-10, 1998
Research4 citationsCompiling the HAL variable to Mercury
B Demoen, M García de la Banda, W Harvey, K Mariott, P Schachte, ...
CW Reports, 8-8, 1998
Research2 citationsGuest Editors' introduction: Constraint logic programming
K Marriott, PJ Stuckey
JOURNAL OF LOGIC PROGRAMMING 37 (1-3), VII-VIII, 1998
Machine LearningUsing Constraints for Flexible Document Layout
A Borning, R Lin, K Marriott, P Stuckey
on Reuse of Web-based Information, 99, 1998
Machine Learning
Learn the science behind the engine
“Forecasting for Data Scientists”, a free 30-chapter video course by co-founder Dr Christoph Bergmeir, from fundamentals to advanced deep learning.