One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are dependent on the policy. Recent works constrained RL have developed methods ensure enforced even at learning time while maximizing overall value of Unfortunately, as demonstrated our experimental results, such approaches do not perform well complex multi-level tasks, with longer episode lengths or...

Chemical computing – using chemical reactions to perform computations – is a promising way to solve computationally intensive problems. Chemical computing is promising because it has the potential of using up to 10 molecules as processors working in parallel – and thus, has a potential of an enormous speedup. Unfortunately, for hard-to-solve (NPcomplete) problems a natural chemical computing ap...

Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper we propose an algorithmic solution that involves a new representation for the knowledge required to a...

We study a class of non-convex optimization problems involving sigmoid functions. We show that sigmoid functions impart a combinatorial element to the optimization variables and make the global optimization computationally hard. We formulate versions of the knapsack problem, the generalized assignment problem and the bin-packing problem with sigmoid utilities. We merge approximation algorithms ...

In this paper, we investigate whether hard combinatorial problems such as the Hamiltonian circuit problem HCP (an NP-complete problem from graph theory) can be practically solved by transformation to the propositional satissability problem (SAT) and application of fast universal SAT-algorithms like GSAT to the transformed problem instances. By using the eecient transformation method proposed by...

• Unlike linear programming problems, integer programming problems are very difficult to solve. In fact, no efficient general algorithm is known for their solution. • Given our inability to solve integer programming problems efficiently, it is natural to ask whether such problems are inherently “hard”. Complexity theory, offers some insight on this question. It provides us with a class of probl...

We study provably effective and efficient data reduction for a class of NP-hard graph modification problems based on vertex degree properties. We show fixed-parameter tractability for NP-hard graph completion (that is, edge addition) cases while we show that there is no hope to achieve analogous results for the corresponding vertex or edge deletion versions. Our algorithms are based on transfor...

Continuous developments in the theory of computational complexity as applied to scheduling problems have revealed the existence of an amazing world of hard problems either all or none of which are solvable in polynomial time. In this paper, we study the structures of sequences and schedules in classical shop scheduling problems where one of the major tasks is to minimize certain regular objecti...

The computational problem of distinguishing two quantum channels is central to quantum computing. It is a generalization of the well-known satisfiability problem from classical to quantum computation. This problem is shown to be surprisingly hard: it is complete for the class QIP of problems that have quantum interactive proof systems, which implies that it is hard for the class PSPACE of probl...