arXiv subjects

Verena Klös

Publications and source records attributed to Verena Klös.

5 recordsLinked to original sources

arXiv2025

Information Inference Diagrams: Complementing Privacy and Security Analyses Beyond Data Flows

This work introduces Information Inference Diagrams (I2Ds), a modeling framework aiming to complement existing approaches for privacy and security analysis of distributed systems. It is intended to support established threat modeling processes. Our approach is designed to be compatible with Data Flow Diagrams~(DFDs), which form the basis of many established techniques and tools. Unlike DFDs, I2Ds represent information propagation, going beyond mere data flows to enable more formal reasoning in threat modeling while remaining practical. They define inference and sharing (flow) relations on information items to model how information moves through a system. To this end, we provide formal definitions for information items, entities, and flows. By introducing classes as a type system, our formal rules are both generic and allow conformance to existing vocabularies. We demonstrate the applicability of I2Ds through examples, that showcase their versatility in system analysis.

cs.CR↗

arXiv2025

Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain

When robots perform complex and context-dependent tasks in our daily lives, deviations from expectations can confuse users. Explanations of the robot's reasoning process can help users to understand the robot intentions. However, when to provide explanations and what they contain are important to avoid user annoyance. We have investigated user preferences for explanation demand and content for a robot that helps with daily cleaning tasks in a kitchen. Our results show that users want explanations in surprising situations and prefer concise explanations that clearly state the intention behind the confusing action and the contextual factors that were relevant to this decision. Based on these findings, we propose two algorithms to identify surprising actions and to construct effective explanations for Belief-Desire-Intention (BDI) robots. Our algorithms can be easily integrated in the BDI reasoning process and pave the way for better human-robot interaction with context- and user-specific explanations.

cs.RO↗

arXiv2022

From Specification Models to Explanation Models: An Extraction and Refinement Process for Timed Automata

Autonomous systems control many tasks in our daily lives. To increase trust in those systems and safety of the interaction between humans and autonomous systems, the system behaviour and reasons for autonomous decision should be explained to users, experts and public authorities. One way to provide such explanations is to use behavioural models to generate context- and user-specific explanations at run-time. However, this comes at the cost of higher modelling effort as additional models need to be constructed. In this paper, we propose a high-level process to extract such explanation models from system models, and to subsequently refine these towards specific users, explanation purposes and situations. By this, we enable the reuse of specification models for integrating self-explanation capabilities into systems. We showcase our approach using a running example from the autonomous driving domain.

cs.SE↗

arXiv2021

Cases for Explainable Software Systems:Characteristics and Examples

The need for systems to explain behavior to users has become more evident with the rise of complex technology like machine learning or self-adaptation. In general, the need for an explanation arises when the behavior of a system does not match the user's expectations. However, there may be several reasons for a mismatch including errors, goal conflicts, or multi-agent interference. Given the various situations, we need precise and agreed descriptions of explanation needs as well as benchmarks to align research on explainable systems. In this paper, we present a taxonomy that structures needs for an explanation according to different reasons. We focus on explanations to improve the user interaction with the system. For each leaf node in the taxonomy, we provide a scenario that describes a concrete situation in which a software system should provide an explanation. These scenarios, called explanation cases, illustrate the different demands for explanations. Our taxonomy can guide the requirements elicitation for explanation capabilities of interactive intelligent systems and our explanation cases build the basis for a common benchmark. We are convinced that both, the taxonomy and the explanation cases, help the community to align future research on explainable systems.

cs.SE↗

arXiv2019

Towards Self-Explainable Cyber-Physical Systems

With the increasing complexity of CPSs, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the MAB-EX framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.

cs.AI↗