Konstantin Schekotihin

Alpen-Adria-Universität Klagenfurt · Institute of Artificial Intelligence and Cybersecurity

Before failure analysis (FA) can start, a product must get from the customer to the correct location, which is not always trivial, especially in larger companies with many FA labs. Automating and optimizing this routing, therefore reducing manual labor, misrouting, and turnaround time, requires the development of problem-solving methods utilizing b...

In view of the high total cost of semiconductor manufacturing assets, respective equipment needs to be as productive as possible. To avoid needless idling and unnecessary downtime, scheduling and maintenance strategies are important in practice. This paper presents a novel approach to reduce the substantial setup costs inherent to ion implantation...

During the activity in the Failure Analysis (FA) laboratory, all corresponding findings and conclusions are included in a series of documents known as the FA reports. They shall, in the first place, inform the requestor about the analysis results. But additionally, they shall provide information to solve similar cases. Therefore, these documents pl...

Failure Analysis (FA) uses combinations of a multitude of methods to identify and localize failures in semiconductor devices. The performance or efficiency of a FA lab is often measured by throughput or the total time needed to complete an analysis. These KPIs (Key Performance Indicators) can be optimized, but sequences of executed methods might be...

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). While CP solvers manage to find optimal or near-optimal solutions for small instances, they do not scale well to large ones, i.e., they require long computation t...

The ability to efficiently solve hard combinatorial optimization problems is a key prerequisite to various applications of declarative programming paradigms. Symmetries in solution candidates pose a significant challenge to modern optimization algorithms since the enumeration of such candidates might substantially reduce their performance. This pap...

In the last decade, the need for storing videos from cataract surgery has increased significantly. Hospitals continue to improve their imaging and recording devices (e.g., microscopes and cameras used in microscopic surgery, such as ophthalmology) to enhance their post-surgical processing efficiency. The video recordings enable a lot of user-cases...

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). While CP solvers manage to find optimal or near-optimal solutions for small instances, they do not scale well to large ones, i.e., they require long computation t...

This article provides a systematic overview of knowledge-based and machine-learning AI methods and their potential for use in automated testing, defect identification, fault prediction, root cause analysis, and equipment scheduling. It also discusses the role of decision-making rules, image annotations, and ontologies in automated workflows, data s...

Semiconductor manufacturing is a notoriously complex and costly multi-step process involving a long sequence of operations on expensive and quantity-limited equipment. Recent chip shortages and their impacts have highlighted the importance of semiconductors in the global supply chains and how reliant on those our daily lives are. Due to the investm...

Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur...

The development of intelligent assistants helping Failure Analysis (FA) engineers in their daily work is essential to any digitalization strategy. In particular, these systems must solve various computer vision or natural language processing problems to select the most critical information from heterogeneous data, like images or texts, and present...

Failure Analysis (FA) is a complex activity that requires careful and complete documentation of all findings and conclusions to preserve knowledge acquired by engineers in this process. Modern FA systems store this data in text or image formats and organize it in databases, file shares, wikis, or other human-readable forms. Given a large volume of...

Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur...

Scheduling methods are important for effective production and logistics management, where tasks need to be allocated and performed with limited resources. In particular, 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...

Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable ins...

Scheduling is an important problem for many applications, including manufacturing, transportation, or cloud computing. Unfortunately, most of the scheduling problems occurring in practice are intractable and, therefore, solving large industrial instances is very time-consuming. Heuristic-based dispatching methods can compute schedules in an accepta...

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. In this paper, we propose problem decomposition into time windows whose operations can be successively scheduled...

Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily-solvable ins...

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced pro...

Scheduling is a crucial problem appearing in various domains, such as manufacturing, transportation, or healthcare, where the goal is to schedule given operations on available resources such that the operations are completed as soon as possible. Unfortunately, most scheduling problems cannot be solved efficiently, so that research on suitable appro...

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced pro...

Fault analysis is a complex task that requires electrical engineers to perform various analyses to detect and localize a physical defect. The analysis process is very knowledge-intensive and must be precisely documented to report the issue to customers as well as to ensure the best possible reuse of the acquired experience in similar future analyse...

In their daily work, engineers in the Failure Analysis (FA) laboratory generate numerous documents reporting all their tasks, findings, and conclusions regarding every device they are handled. This data stores valuable knowledge for the laboratory that other experts can consult, however, the nature of it, as individual reports reporting concrete de...

Efficient omission of symmetric solution candidates is essential for combinatorial problem solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced pro...

Many complex activities in production cycles, such as quality control or fault analysis, require highly experienced specialists to perform various operations on (semi)finished products using different tools. In practical scenarios, the next operation selection is complicated since each expert has only a local view on the entire set of operations to...

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. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit....

Software product metrics allow practitioners to improve their products and to optimize development processes based on quantifiable characteristics of source code. To facilitate similar benefits for spreadsheet programs, researchers proposed various product metrics for spreadsheets over the last decades. However, to our knowledge, no comprehensive o...

Many complex activities of production cycles, such as quality control or fault analysis, require highly experienced specialists to perform various operations on (semi)finished products using different tools. In practical scenarios, the selection of a next operation is complicated, since each expert has only a local view on the total set of operatio...

Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Appli...

Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Appli...

Electronic spreadsheets are widely used in organizations for various data analytics and decision-making tasks. Even though faults within such spreadsheets are common and can have significant negative consequences, today's tools for creating and handling spreadsheets provide limited support for fault detection, localization, and repair. Being able t...

Domain-specific heuristics are an important technique for solving combinatorial problems efficiently. We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP). Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e....

Domain-specific heuristics are an important technique for solving combinatorial problems efficiently. We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP). Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e....

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. 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. However, the...

Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. 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. However, the...

Real-world semantic or knowledge-based systems can become large and complex, e.g., in the biomedical domain. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontolog...

Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontolo...

Faults in spreadsheets can represent a major risk for businesses. To minimize such risks, various automated testing and debugging approaches for spreadsheets were proposed. In such approaches, often one main assumption is that the spreadsheet developer is able to indicate if the outcomes of certain calculations correspond to the intended values. Th...

Answer set programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning. Despite that ASP features a simple syntax and an intuitive semantics, errors are common during the development of ASP programs. In this paper we propose a novel debugging approach allowing for interactive...

Answer Set Programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning. Despite that ASP features a simple syntax and an intuitive semantics, errors are common during the development of ASP programs. In this paper we propose a novel debugging approach allowing for interactive...

Automated problem solving in combination with declarative specifications of search-problems have shown to substantially improve the implementation and maintenance costs as well as the man-machine interaction of deployed industrial applications. The knowledge representation and reasoning (KRR) framework of answer set programming (ASP) offers a rich...

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in p...

Model-Based Diagnosis (MBD) is a general-purpose computational approach to determine why a system under observation, e.g., an electronic circuit or a software program, does not behave as expected. MBD approaches utilize knowledge about the system's expected behavior if all of its components work correctly. In case of an unexpected behavior they sys...

In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of...

Model-Based Diagnosis (MBD) is a principled approach to fault localization in any type of system that can be described in a formal structured way. Knowledge Base Debugging (KBD) draws on concepts from MBD to find faults in a monotonic knowledge base. We show that KBD is a generalization of MBD in that any MBD problem can be reduced to a KBD problem...

Model-Based Diagnosis deals with the identification of the real cause of a system's malfunction based on a formal system model and observations of the system behavior. When a malfunction is detected, there is usually not enough information available to pinpoint the real cause and one needs to discriminate between multiple fault hypotheses (called d...

In many model-based diagnosis applications it is impossible to provide such a set of observations and/or measurements that allow to identify the real cause of a fault. Therefore, diagnosis systems often return many possible candidates, leaving the burden of selecting the correct diagnosis to a user. Sequential diagnosis techniques solve this proble...

The CDCL algorithm is the leading solution adopted by state-of-the-art solvers for SAT, SMT, ASP, and others. Experiments show that the performance of CDCL solvers can be significantly boosted by embedding domain-specific heuristics, especially on large real-world problems. However, a proper integration of such criteria in off-the-shelf CDCL implem...

Answer Set Programming (ASP) is an expressive knowledge representation and reasoning framework. Due to its rather simple syntax paired with high-performance solvers, ASP is interesting for industrial applications. However, to err is human and thus debugging is an important activity during the development process. Therefore, tools for debugging non-...

Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture. Importantly, CCN routers use various caching strategies to locally cache content frequently requested by end users. However, it is unclear which content shall be stored and when it should be replaced. In this work...

Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture. Importantly, CCN routers may locally cache frequently requested content in order to speed up delivery to end users. Thus, the issue of caching strategies arises, i.e., which content shall be stored and when it sho...

Answer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and...

Model-Based Diagnosis (MBD) is a principled and domain-independent way of analyzing why a system under examination is not behaving as expected. Given an abstract description (model) of the system's components and their behavior when functioning normally, MBD techniques rely on observations about the actual system behavior to reason about possible c...

Answer Set Programming (ASP) is an expressive paradigm for problem solving. Although the basic syntax of ASP is not particularly difficult, the identification of (even trivial) mistakes may be painful and absorb a lot of time. The development of programs can be made faster and comfortable by resorting to an effective program debugger. In this paper...

Most of contemporary software systems are implemented using an object-oriented approach. Modeling phases – during which software engineers analyze requirements to the future system using some modeling language – are an important part of the development process, since modeling errors are often hard to recognize and correct. In this paper we present...

Most of contemporary software systems are implemented using an object-oriented approach. Modeling phases -- during which software engineers analyze requirements to the future system using some modeling language -- are an important part of the development process, since modeling errors are often hard to recognize and correct. In this paper we presen...

Model-Based Diagnosis techniques have been successfully applied to support a variety of fault-localization tasks both for hardware and software artifacts. In many applications, Reiter's hitting set algorithm has been used to determine the set of all diagnoses for a given problem. In order to construct the diagnoses with increasing cardinality, Reit...

Broad application of answer set programming (ASP) for declarative problem solving requires the development of tools supporting the coding process. Program debugging is one of the crucial activities within this process. Modern ASP debugging approaches allow efficient computation of possible explanations of a fault. However, even for a small program...

Broad application of answer set programming (ASP)for declarative problem solving requires the development of tools supporting the coding process. Program debugging is one of the crucial activities within this process. Recently suggested ASP debugging approaches allow efficient computation of possible explanations of a fault. However, even for a sma...

In recent years, researchers have developed a number of tech- niques to assist the user in locating a fault within a spread- sheet. The evaluation of these approaches is often based on spreadsheets into which artificial errors are injected. In this position paper, we summarize different shortcomings of these forms of evaluations and sketch possible...

Sequential diagnosis methods compute a series of queries for discriminating between diagnoses. Queries are answered by some oracle such that eventually the set of faults is identified. The computation of queries is based on the generation of a set of most probable diagnoses. However, in diagnosis problem instances where the number of minimal diagno...