Mathias Uta
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Learning constraint orderings for direct diagnosis Open
The ability to efficiently resolve conflicts in interactive constraint-based applications is critical for user experience and system reliability. Conflict resolution can be regarded as a specific type of explanation, often denoted as diagn…
Learning Constraint Orderings for Direct Diagnosis Open
Given an inconsistent set of constraints, it is important to efficiently resolve the underly-ing conflicts. Conflict resolution can be regarded as a specific type of explanation, oftendenoted as diagnosis. Efficiency is of high importance …
INFORMEDQX: Informed Conflict Detection for Over-Constrained Problems Open
Conflict detection is relevant in various application scenarios, ranging from interactive decision-making to the diagnosis of faulty knowledge bases. Conflicts can be regarded as sets of constraints that cause an inconsistency. In many sce…
Knowledge-based recommender systems: overview and research directions Open
Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-ba…
WipeOutR Open
Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two …
Towards psychology-aware preference construction in recommender systems: Overview and research issues Open
User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for exam…
Configuring Multiple Instances with Multi-Configuration Open
Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of …
An overview of machine learning techniques in constraint solving Open
Constraint solving is applied in different application contexts. Examples thereof are the configuration of complex products and services, the determination of production schedules, and the determination of recommendations in online sales s…
DirectDebug: A software package for the automated testing and debugging of feature models Open
Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testi…
Consistency-based Merging of Variability Models Open
Globally operating enterprises selling large and complex products and services often have to deal with situations where variability models are locally developed to take into account the requirements of local markets. For example, cars sold…
DirectDebug: Automated Testing and Debugging of Feature Models Open
Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for q…