I worked on this with Peter Gardenfors in my PhD thesis. If someone is interested in a mathematical model of conceptual spaces which is not machine-learning oriented, you can check this out:…
Luckily we do. Our diagrams are sound and complete for free symmetric monoidal categories, meaning that every diagram can be converted to a morphism in a category. :)
This is exactly why we use petri nets and then we map them to a semantics. Drafting a net represents the "diagram part". You draw what your code is supposed to do as you would do on a whiteboard. But this time you have…
What you say is true. Text is way more expressive, but sometimes it is difficult to spot the overall code structure just by using text. What Statebox does is this: It implements Petri nets as categories and maps them…
> Visual programming tools attempt to map logic to a (usually) 2D domain where there is no natural or intuitive general mapping. The representation has both too many degrees of freedom (arbitrary positions of nodes in…
I worked on this with Peter Gardenfors in my PhD thesis. If someone is interested in a mathematical model of conceptual spaces which is not machine-learning oriented, you can check this out:…
Luckily we do. Our diagrams are sound and complete for free symmetric monoidal categories, meaning that every diagram can be converted to a morphism in a category. :)
This is exactly why we use petri nets and then we map them to a semantics. Drafting a net represents the "diagram part". You draw what your code is supposed to do as you would do on a whiteboard. But this time you have…
What you say is true. Text is way more expressive, but sometimes it is difficult to spot the overall code structure just by using text. What Statebox does is this: It implements Petri nets as categories and maps them…
> Visual programming tools attempt to map logic to a (usually) 2D domain where there is no natural or intuitive general mapping. The representation has both too many degrees of freedom (arbitrary positions of nodes in…