Please note this workshop will skip Thursday, July 4 (UC holiday). The workshop will take place Monday, July 1 - Wednesday, July 3, and concludes Friday, July 5. 

The workshop aims to bring together researchers interested in the broad area of synthesis of computational models and systems. Software controls an increasingly large part of infrastructure and cyber-physical systems (CPSs), such as sensors, cars, and data centers. On the one hand, we therefore need to automate software development as much as possible, which is facilitated by model-based design. On the other hand, we need to automate model and system creation, as well as its adaptation and repair.

The synthesis problem refers to the collection of algorithmic techniques that automatically construct software artifacts (models, controllers, programs) from specifications or behavioral observations. The specifications are usually stated in a logical formalism and can be quantitative, and behavioral observations include examples of inputs and outputs and time series data. One advantage of synthesis is that it yields an artifact that is correct by construction, which avoids the need to fix the bug once it has been found using verification. An issue with synthesis is that it does not have a canonical statement of the problem, which has given rise to a variety of approaches, each characterized by the type of inputs considered: reactive synthesis, template-based program synthesis, controller synthesis from (quantitative) temporal logic specifications, syntax-guided synthesis, counterexample-guided inductive synthesis, assume-guarantee component synthesis, and parameter synthesis. An emerging area is program synthesis and repair using machine learning, which encompasses automata learning and the use of deep and reinforcement learning.

The research area of synthesis has seen great progress in the past decade, stemming from novel algorithms and progress in neighboring areas such as constraint solving, game theory and planning. 

This workshop will build on these advances and will aim to build connections between different subareas of synthesis: from program synthesis and model synthesis to methods based on machine learning. The challenging problem areas that are synthesis for networks and distributed systems are the primary target applications.

If you require special accommodation, please contact our access coordinator at simonsevents [at] berkeley.edu with as much advance notice as possible.

Invited Participants

S Akshay (Indian Institute of Technology Bombay), Rajeev Alur (University of Pennsylvania), Suguman Bansal (Georgia Institute of Technology), Pascal Bergsträßer (University of Kaiserslautern), Rastislav Bodik (Google DeepMind), Borzoo Bonakdarpour (Michigan State University), Sarah E. Chasins (UC Berkeley), Loris D'antoni (University of Wisconsin - Madison), Bernd Finkbeiner (CISPA Helmholtz Center for Information Security), William Hallahan (Binghamton University), Orna Kupferman (Hebrew University), Elaine Li (New York University), Anthony Lin (TU Kaiserslautern), Kuldeep S. Meel (University of Toronto), Anca Muscholl (University of Bordeaux), Necmiye Ozay (Univ. of Michigan), Corina Pasareanu (CMU CyLab/NASA Ames), Armando Solar-Lezama (MIT), Stefan Szeider (TU Wien), Moshe Vardi (Rice University), Igor Walukiewicz (University of Bordeaux), Sandra Zilles (University of Regina)