Dec 21, 2020 · There are several special cases of network problems, such as the shortest path problem, minimum cost flow problem, assignment problem and transportation problem. [6] …

An assignment is complete when every variable is mentioned. A solution to a CSP is a complete assignment that satisfies all constraints. Some CSPs require a solution that maximizes an …

Oct 27, 2022 · We model the two problems as node assignment problems and design the corresponding constraint-aware loss function for each so that the model can be directly …

K-Consistency: For each k nodes, any consistent assignment to k-1 can be extended to the kth node. Claim: strong n-consistency means we can solve without backtracking! Why?

A constrained optimization problem is an optimization problem that also has hard constraints. The set of assignments that does not violate a constraint is the set of feasible assignments. The …

For optimization problems, there are no well-defined goal nodes. X1, X2, ... , Xn . Xi has an associated domain of possible values. which specify legal combinations of values for these …

Binary CSP: each constraint relates at most 2 variables (i.e., colors of 2 states) Constraint graph: nodes are variables, arcs show constraints General-purpose CSP algorithms use the graph …

We train a reinforcement learning agent to find near optimal solutions to the problem by minimizing total cost associated with the assignments while maintaining hard constraints. We …

Which variable should be assigned next? In what order should its values be tried? Can we detect inevitable failure early? Can we take advantage of problem structure?

Constraint satisfaction problems (CSPs) Standard search problem: state is a “black box”—any old data structure that supports goal test, eval, successor CSP: state is defined by variables X i …