How convenient would it be to have an AI that relieves us programmers from the burden of coding? Program synthesis is a technique that achieves exactly that: it automatically generates simple programs that meet a given set of examples or adhere to a provided specification. This is often done by enumerating all programs in the search space and returning the first program that satisfies the requirements. However, these algorithms frequently enumerate redundant programs because of symmetries in the search space. We propose a new constraint discovery system that is able to detect these symmetries in a language and systematically generate symmetry-breaking constraints for them. To test these constraints, we implemented a novel, re-usable framework for program synthesis called Herb.jl. The generated constraints are shown to cut down search spaces to less than 25% of the original size and reduce the enumeration time by a factor of 3. Furthermore, this approach is extended to automatically discover semantic specifications without needing an expert. The effectiveness of these specifications is evaluated with an existing specification-based synthesizer, which shows that adding these specifications is an effective way to cut the synthesis time in half for domains where expert-defined specifications are not available. Together, these approaches demonstrate the effectiveness of extracting additional information from a language and applying it during enumeration.

For the Multi-Agent Pathfinding (MAPF) problem, a set of non-colliding paths must be found for multiple agents. In Multi-Agent Pathfinding with Matching (MAPFM), this problem is extended: agents and goals are added to a team and each agent has to navigate to a goal that belongs to the same team. In this paper, two extensions of the EPEA* MAPF solver will be discussed that enable it to solve MAPFM problems. The first extension modifies the EPEA* algorithm to directly allow it to solve MAPFM problem instances. The second extension generates all possible goal assignments for each agent and runs EPEA* on these assignments. This last extension is shown to have a superior performance in most cases. The second extension is also compared to extensions of other MAPF algorithms.