Many computer-based scientific or engineering applications need to store, analyze, and manipulate large data. Often, this data has enough structure that specialized data structures and algorithms can have dramatically smaller memory and time requirements than explicit approaches. An important such case is symbolic verification of hardware and software, where, traditionally, binary decision diagrams (BDDs) have been successfully employed to study systems with enormous state spaces. Several software libraries for BDDs have arisen to support these operations, and BDDs have been applied to diverse applications as a means to exploit structure that is often hidden.
However, in the past decade, decision diagram theory has continued to advance, by generalizing BDDs to variants with multi-way decisions (MDDs), multi-way or numerical outcomes, or edge values to encode real-valued data, and by proposing a variety of reduction rules to change (and often shrink) the decision diagram shape, as well as many important algorithmic improvements. Unfortunately, decision diagram libraries have not kept up with these theoretical advances.
The proposed work seeks to fill this gap, by merging and expanding two existing prototype libraries, MEDDLY (designed by PI Miner) and TEDDY (designed by Co-PI Ciardo), into a powerful, next-generation decision diagram library that supports a more general theory of decision diagrams. The new library will encompass (1) non-binary variables, including a-priori unbounded discrete domains and even infinite domains under certain restrictions, (2) non-boolean function values, attached either to terminal nodes or to the edges of the decision diagram, and (3) a more general definition of canonicity that includes a wide spectrum of reduction rules. Several proposed activities will help smooth the learning curve for users adopting this library, from proven methods such as user manuals, tutorials, examples, wikis, and user groups, to novel ones such as the development of visualization techniques to aid the debugging and understanding of decision diagrams.