5 citations · 9 across the 6 of their papers we have counts for
6 papers · 1 filter
An XAI View on Explainable ASP: Methods, Systems, and Perspectives
Thomas Eiter, Tobias Geibinger, Zeynep G. Saribatur
Answer Set Programming (ASP) is a popular declarative reasoning and problem solving approach in symbolic AI. Its rule-based formalism makes it inherently attractive for explainable…
Why this and not that? A Logic-based Framework for Contrastive Explanations
Tobias Geibinger, Reijo Jaakkola, Antti Kuusisto +2
We define several canonical problems related to contrastive explanations, each answering a question of the form ''Why P but not Q?''. The problems compute causes for both P and Q,…
ASP-FZN: A Translation-based Constraint Answer Set Solver
Thomas Eiter, Tobias Geibinger, Tobias Kaminski +2
We present the solver asp-fzn for Constraint Answer Set Programming (CASP), which extends ASP with linear constraints. Our approach is based on translating CASP programs into the s…
Explainable Answer-set Programming
Tobias Geibinger
The interest in explainability in artificial intelligence (AI) is growing vastly due to the near ubiquitous state of AI in our lives and the increasing complexity of AI systems. An…
Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling
Thomas Eiter, Tobias Geibinger, Nysret Musliu +3
We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop pr…
Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling
Tobias Geibinger, Florian Mischek, Nysret Musliu
In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns i…