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Author

Nourhan Ehab

ORCID: 0000-0002-0345-6279
Observed ORCID: 0000-0002-0345-6279
OpenAlex ID: A5056213646
Leveraging Context-Sensitivity for User-Centered Explainability
2025 · Yasmeen Khaled, Nourhan Ehab · The European Journal on Artificial Intelligence · 4
Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning
2024 · Karim Wahdan, Nourhan Ehab, et al. · International Conference on Agents and Artificial Intelligence (ICAART) · 4
LogAG: An algebraic non-monotonic logic for reasoning with graded propositions
2020 · Nourhan Ehab, Haythem O. Ismail · Annals of Mathematics and Artificial Intelligence · 3
Dynamic Path Planning for Autonomous Vehicles: A Neuro-Symbolic Approach
2024 · Omar Elrasas, Nourhan Ehab, et al. · International Conference on Agents and Artificial Intelligence (ICAART) · 2
Towards a Unified Algebraic Framework for Non-Monotonicity.
2018 · Nourhan Ehab, Haythem O. Ismail · 2
LogAG: An Algebraic Non-Monotonic Logic for Reasoning with Uncertainty.
2017 · Nourhan Ehab, Haythem O. Ismail · 2
Caïssa AI: A Neuro-Symbolic Chess Agent for Explainable Move Suggestion and Grounded Commentary
2025 · Mazen Soliman, Nourhan Ehab · Lecture notes in computer science · 1
Hybrid Neuro-Symbolic Learning Agents: A Graph-Theoretic Approach to Interpretable Reinforcement Learning
2025 · Reem Alansary, Nourhan Ehab · Communications in computer and information science · 1
A Framework for Explaining Accident Scenarios for Self-Driving Cars
2023 · Omar Wahbi, Yasmin Mansy, et al. · International Conference on Agents and Artificial Intelligence (ICAART) · 1
Correction to: LogAG: An algebraic non-monotonic logic for reasoning with graded propositions
2020 · Nourhan Ehab, Haythem O. Ismail · Annals of Mathematics and Artificial Intelligence · 1
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A Unified Algebraic Framework for Non-Monotonicity
2019 · Nourhan Ehab, Haythem O. Ismail · Electronic Proceedings in Theoretical Computer Science · 1
PDF
Algebraic Semantics for Graded Propositions.
2015 · Haythem O. Ismail, Nourhan Ehab · 1
A Modular Neuro-Symbolic Framework for Ethical Path Planning in Autonomous Vehicles
2026 · Manar Hatem, Nourhan Ehab, et al.
An Agentic Outlook on Neuro-Symbolic AI: Towards Modular, Adaptive, and Explainable AI Systems
2026 · Nourhan Ehab · International Conference on Agents and Artificial Intelligence (ICAART)
A Retrieval-Augmented Generation Based Approach for Contextualized & Complete Explanations
2026 · Salma Kishk, Nourhan Ehab, et al. · International Conference on Agents and Artificial Intelligence (ICAART)
Learn, Optimize, Explain: A Neuro-Symbolic Advisor for Personal Finance
2025 · Youssef Ayman, Nourhan Ehab · Lecture notes in computer science
Towards Knowledge-Augmented Agents for Efficient and Interpretable Learning in Sequential Decision Problems
2024 · Reem Alansary, Nourhan Ehab · International Conference on Agents and Artificial Intelligence (ICAART)
ConEX: A Context-Aware Framework for Enhancing Explanation Systems
2024 · Yasmeen Khaled, Nourhan Ehab · International Conference on Agents and Artificial Intelligence (ICAART)
The Problem of Many Vehicles: An Explainable System for Autonomous Multi-agent Accidents
2024 · Yasmin Mansy, Nourhan Ehab, et al. · Lecture notes in networks and systems
Towards Accountable Self-Driving Cars: A Framework for Identifying the Cause of Autonomous Traffic Collisions
2023 · Yasmin Mansy, Nourhan Ehab, et al. · IEEE World Forum on Internet of Things (WF-IoT)
Conflicting Moral Codes for Self-Driving Cars: The Single Car Case
2023 · Ahmed Ibrahim, Yasmin Mansy, et al. · International Conference on Agents and Artificial Intelligence (ICAART)
Ethical Self-driving Cars: Resolving Conflicting Moral Decisions Among Groups of Vehicles
2023 · Khaled T. Aly, Yasmin Mansy, et al. · Communications in computer and information science
Towards Explainable Practical Agency
2021 · Nourhan Ehab, Haythem O. Ismail · Lecture notes in computer science
Reasoning with Artificial Mental States: An Algebraic Approach.
2020 · Nourhan Ehab, Haythem O. Ismail
Works count: 24
Citation count: 23
H-index: 3
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