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Found 18 papers, 5 papers with code
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming
Constraint Programming (CP) is a declarative programming paradigm that allows for modeling and solving combinatorial optimization problems, such as the Job-Shop Scheduling Problem (JSSP).
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning
Semiconductor manufacturing is a notoriously complex and costly multi-step process involving a long sequence of operations on expensive and quantity-limited equipment.
Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming
Additionally, we use our approach to implement informed} search with A*, which is tackled within ASP for the first time.
Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling
The Job-shop Scheduling Problem (JSP) is a well-known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible.
Efficient lifting of symmetry breaking constraints for complex combinatorial problems
Many industrial applications require finding solutions to challenging combinatorial problems.
Lifting Symmetry Breaking Constraints with Inductive Logic Programming
Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving.
A Reinforcement Learning Environment For Job-Shop Scheduling
Scheduling is a fundamental task occurring in various automated systems applications, e. g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste.
Solving a Multi-resource Partial-ordering Flexible Variant of the Job-shop Scheduling Problem with Hybrid ASP
In this paper, we consider a Multi-resource Partial-ordering Flexible Job-shop Scheduling (MPF-JSS) problem where partially-ordered sequences of operations must be scheduled on multiple required resources, such as tools and specialists.
Managing caching strategies for stream reasoning with reinforcement learning
In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one.
Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP
Domain-specific heuristics are an important technique for solving combinatorial problems efficiently.
A Distributed Approach to LARS Stream Reasoning (System paper)
Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed.
Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?
Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success.
Debugging Non-Ground ASP Programs: Technique and Graphical Tools
Answer Set Programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning.
A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis
To this end, Sequential Diagnosis approaches ask an oracle for additional system measurements.
Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis.
Driving CDCL Search
The CDCL algorithm is the leading solution adopted by state-of-the-art solvers for SAT, SMT, ASP, and others.
An integrated Graphical User Interface for Debugging Answer Set Programs
Furthermore, the debugging interface is integrated in ASPIDE, a rich IDE for answer set programs.
Stream Reasoning-Based Control of Caching Strategies in CCN Routers
Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture.
