I appreciate the effort, but you want to study agent that solve problem by using mutual program analysis and self-modification (in the form of generating different successors). Will come up with non-trivial examples where such strategies pay off in your simulator? It seems quite hard to me. Due to technical limitations, anything involving automated theorem proving or complex planning is going to be off the table.
In this report, the register machines use a very simple instruction set which we call the constree language. A full implementation can be found in Appendix B. However, when modelling concrete decision problems in Botworld, we may choose to replace this simple language by something easier to use. (In particular, many robot programs will need to reason about Botworld’s laws. Encoding Botworld into the constree language is no trivial task.)
I appreciate the effort, but you want to study agent that solve problem by using mutual program analysis and self-modification (in the form of generating different successors). Will come up with non-trivial examples where such strategies pay off in your simulator?
It seems quite hard to me. Due to technical limitations, anything involving automated theorem proving or complex planning is going to be off the table.
From the technical report:
So next version will accept robot programs written in Coq, I suppose ;)