Daan Apeldoorn

19 papers Misc 1Journal 4Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	KI in der Schule und im interdisziplinären Studium - Regelbasierte Systeme und Wissensrepräsentation im Kontext von Agenten. Informatische Bildung in Schulen (IBiS) Daan Apeldoorn, Andre Thevapalan
2025	J	`jnl`	Comprehensible Extraction of Knowledge Bases for Learning Agents in Games. Künstliche Intell. Daan Apeldoorn
2025	—	`conf`	Corrigendum to Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data. GMDS Xenia Heilmann, Valentin Henkys, Daan Apeldoorn, Konstantin Strauch, Bertil Schmidt, Timm Lilienthal, Torsten Panholzer
2024	—	`conf`	AbstractSwarm Multi-Agent Logistics Competition: Multi-Agent Collaboration for Improving A Priori Unknown Logistics Scenarios. GECCO Companion Daan Apeldoorn, Alexander Dockhorn, Torsten Panholzer
2024	—	`conf`	Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data. GMDS Xenia Heilmann, Daan Apeldoorn, Konstantin Strauch, Bertil Schmidt, Timm Lilienthal, Torsten Panholzer
2023	—	`conf`	Comparison and Incorporation of Reasoning and Learning Approaches for Cancer Therapy Research. GMDS Andre Thevapalan, Daan Apeldoorn, Gabriele Kern-Isberner, Ralf Georg Meyer, Mathias Nietzke, Torsten Panholzer
2023	—	`—`	Comprehensible knowledge base extraction for learning agents: practical challenges and applications in games. Daan Apeldoorn
2021	—	`conf`	Automated Creation of Expert Systems with the InteKRator Toolbox. GMDS Daan Apeldoorn, Torsten Panholzer
2021	J	`jnl`	Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning. IEEE Trans. Games Daan Apeldoorn, Alexander Dockhorn
2021	J	`jnl`	KI in der Schule - Teil 1: Einführung in die künstliche Intelligenz. LOG IN Daan Apeldoorn
2021	—	`conf`	Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes. MIWAI Daan Apeldoorn, Lars Hadidi, Torsten Panholzer
2018	—	`conf`	Forward Model Approximation for General Video Game Learning. CIG Alexander Dockhorn, Daan Apeldoorn
2017	—	`conf`	An Agent-Based Learning Approach for Finding and Exploiting Heuristics in Unknown Environments. COMMONSENSE Daan Apeldoorn, Gabriele Kern-Isberner
2017	—	`conf`	Measuring strategic depth in games using hierarchical knowledge bases. CIG Daan Apeldoorn, Vanessa Volz
2017	Misc	`conf`	Towards An Understanding of What is Learned: Extracting Multi-Abstraction-Level Knowledge from Learning Agents. FLAIRS Daan Apeldoorn, Gabriele Kern-Isberner
2016	—	`conf`	When Should Learning Agents Switch to Explicit Knowledge? GCAI Daan Apeldoorn, Gabriele Kern-Isberner
2015	—	`conf`	A Spatio-Temporal Multiagent Simulation Framework for Reusing Agents in Different Kinds of Scenarios. MATES Daan Apeldoorn
2015	—	`conf`	Learning Rules for Cooperative Solving of Spatio-Temporal Problems. DKB/KIK@KI Daan Apeldoorn
2013	—	`conf`	AbstractSwarm - A Generic Graphical Modeling Language for Multi-Agent Systems. MATES Daan Apeldoorn

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.