activity
20172026
most citedFrom Checking to Inference: Actual Causality Computations as Optimization Problems

11 citations · 17 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI

Amjad Ibrahim, Yong Li

As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the traditional boundaries of software sy…

cs.AI2020★ 11 cited

From Checking to Inference: Actual Causality Computations as Optimization Problems

Amjad Ibrahim, Alexander Pretschner

Actual causality is increasingly well understood. Recent formal approaches, proposed by Halpern and Pearl, have made this concept mature enough to be amenable to automated reasonin…

cs.SE2020★ 2 cited

Expressing Accountability Patterns using Structural Causal Models

Severin Kacianka, Amjad Ibrahim, Alexander Pretschner

While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparen…

cs.AI2019★ 4 cited

Extending Causal Models from Machines into Humans

Severin Kacianka, Amjad Ibrahim, Alexander Pretschner +2

Causal Models are increasingly suggested as a means to reason about the behavior of cyber-physical systems in socio-technical contexts. They allow us to analyze courses of events a…

cs.DL2019

Taxonomy-as-a-Service: How To Structure Your Related Work

Mohsen Ahmadvand, Amjad Ibrahim, Felix Huber

Structuring related work is a daunting task encompassing literature review, classification, comparison (primarily in the form of concepts), and gap analysis. Building taxonomies is…

cs.AI2019

Efficiently Checking Actual Causality with SAT Solving

Amjad Ibrahim, Simon Rehwald, Alexander Pretschner

Recent formal approaches towards causality have made the concept ready for incorporation into the technical world. However, causality reasoning is computationally hard; and no gene…