Jaan Tollander de Balsch
Jaan Tollander de Balsch
The visual representation of influence diagrams requires visualizing nodes, information sets, and states. Nodes should have distinct shapes and colors. We should represent them in depth-wise order from left to...
Benchmarks for decision programming on different kinds of influence diagrams. Here are some ideas on what to measure: - Hard lower bound versus soft lower bound with the positive path...
When there are inactive chance states (zero probability) in the model, the model currently creates path probability variables and fixes them to zero. We could avoid creating the fixed variables...
As demonstrated by the *contingent portfolio programming* example, the path utility functions allow using numerical, linear, and integer-linear JuMP expressions as values for path utilities when forming positive path utility...
Nodes and states should have **optional name tags** that would be displayed when printing results. Tags are strings, preferably a maximum of 3 characters long, and default to the node...
We need a rigorous definition of **symmetric** influence diagrams. Influence diagrams which are not symmetric are **asymmetric**. If an influence diagram has inactive chance states, it can be asymmetric, but...
The appendix page should contain the following sections: - **Proofs**: The documentation should be self-contained, that is, not rely on external references for proofs of the formulation. - **Notation**: List...
- [ ] crowd density - [ ] crowd pressure - [ ] velocity # Crowd Density Algorithm Implement algorithms for computing crowd densities using Voronoi diagrams. Steffen, B., &...
Replace `scipy.spatial.Delaunay` triangulation with algorithm that works for arbitrary polygons so that agent placing algorithm supports spawns that `non-convex` and can have `holes`. https://stackoverflow.com/questions/5247994/simple-2d-polygon-triangulation https://en.wikipedia.org/wiki/Polygon_triangulation#Ear_clipping_method