Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an algorithm solve them. Popular algorithms for PPLs, such as sequential Monte Carlo (SMC) Markov chain (MCMC), are built around checkpoints -- relevant events the during execution of a probabilistic program. Deciding location is, in current not done optimally. To this problem...

Automatic programming makes it possible to avoid the tedious task of creating computer programs. In automatic programming, the program is obtained by first specifying the goals which are to be realized by the program. Then, based on this specification, the program is constructed automatically. This chapter describes the idea of one of the methods for automatic function approximation (as a form ...

The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of moving a box from the middle of an irregular shaped room to the wall. We compare the ability of the recently...

Since the development of FORTRAN fifteen years ago we have observed a steady growth in the development of that part of computer science that deals with programming. In particular, there has been an outstanding development in programming languages: we can say more—and in a more natural fashion—to the computer and its attendant service programs to get our tasks accomplished. Our programming langu...

This paper is an overview of current approaches to automatic programming organized around three fundamental questions that must be addressed in the design of any automatic programming system: What does the user see? How does the system work? What does the system know? As an example of a research effort in this area, we focus the Programmerś Apprentice project. Advances in Computers, Volume 37, ...

Most automatic programming research has focused on programs which terminate and which produce output values upon termination. By contrast, programs which operate on streams of data usually do not terminate and usually produce streams of output data during execution. Such stream programs may be specified with a technique which is a generalization of specification techniques for conventional prog...

This paper presents the robust optimization framework in the modeling language YALMIP, which carries out robust modeling and uncertainty elimination automatically, and allows the user to concentrate on the high-level model. While introducing the software package, a brief summary of robust optimization is given, as well as some comments on modeling and tractability of complex convex uncertain op...