Using Ontologies in Failure Analysis

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 analyses. However, writing unambiguous documentation can be complicated for many reasons, such as selecting details and results to be presented in a report, or the naming of terms and their definition. To avoid some of these issues, FA engineers must agree on a clearly defined terminology specifying methods, physical faults and their electrical signatures, tools, and relations between them. Moreover, to allow FA software systems to use this terminology, it must be stored in a format that can be interpreted similarly by both engineers and software. This paper presents an approach that solves these challenges by using an ontology describing FA-relevant terminology using a logic-based representation. The latter guarantees the same interpretation of the defined terms by engineers and software systems, which can use it to perform various tasks like text classification, information retrieval, or workflow verification.

Explainable AI for Workflow Verification in VIPLE

Teaching students the concepts behind computational thinking is a difficult task, often gated by the inherent difficulty of programming languages. In the classroom, teaching assistants may be required to interact with students to help them learn the material. Time spent in grading and offering feedback on assignments removes from this time to help students directly. As such, we offer a framework for developing an explainable Artificial Intelligence that performs automated analysis of student code while offering feedback and partial credit. The creation of this system is dependent on three core components. Those components are a knowledge base, a set of conditions to be analyzed, and a formal set of inference rules. In this paper, we develop such a system for our own language by employing Pi-Calculus and Hoare Logic. Our detailed system can also perform self-learning of rules. Given solution files, the system is able to extract the important aspects of the program and develop feedback that explicitly details the errors students make when they veer away from these aspects. The level of detail and expected precision can be easily modified through parameter tuning and variety in sample solutions.

A One-Pass Tableau-Based Workflow Verification Framework

Workflow management systems (WfMSs) are useful tools for supporting enterprise information systems. Such systems must ensure compliance with guidelines and regulations. While formal verification techniques can be used in the development stages to help ensure behavioral properties of many systems, these techniques are generally not available in workflow tools. We present a framework which models workflows using Petri nets and translates the model to a tableau style model checker. The model checker uses the recently introduced one-pass tableau algorithm and delivers enhanced performance over traditional two-pass strategies in practical applications. A failed tableau will generate a counter model which can aid in debugging. We present a case study involving a health services delivery program, and verify properties written in Computation Tree Logic (CTL). The algorithm can be easily modified to accomodate other specification languages such as timed CTL, logics of beliefs, desires and intentions, temporal description logic, first order logic, and others.

ICN-Based Workflow Model and its Advances

This chapter introduces the basic concepts of information control net (ICN) and its workflow models. In principle, a workflow model is the theoretical basis of a workflow modeling methodology as well as a workflow enactment architecture. Particularly, the workflow model is directly related with how its major components are embodied for implementing the underlying workflow enactment system, too. Accordingly, the authors describe the graphical and formal representations of ICN-based workflow model and its advanced models-role-based model and actor-based model-that can be automatically transformed from the ICN-based workflow model in order to improve their verifiability, maintainability and usability. Conclusively stating, we strongly believe that the ICN-based workflow model and its advanced models be very useful not only for maximizing the quality of workflows but also for strengthening theoretical backgrounds of the recent research issues, such as workflow verification/validation, workflow reengineering, workflow intelligence, workflow mining/rediscovery, and advanced workflow architectures, and so on.