Dimitris Kalles

AAMAS Conference 2026 Conference Paper

From Real-World Images to Agent-Based Crowd Simulations: An End-to-End Pipeline

• Helena G. Theodoropoulou
• Vasilis Zafeiropoulos
• Zoi Lygizou
• Michail Zervas
• Dimitris Kalles
• Chairi Kiourt

This work presents an end-to-end pipeline that transforms a single RGB image into a high-fidelity crowd simulation. The framework integrates multidimensional scene perception and socio-emotional agent intelligence. The platform supports both interactive GUI and GPU-accelerated headless modes for large-scale, complex scenarios. By bridging real-world visual data with autonomous behavioral modeling, this unified, data-driven framework provides a scalable solution for advanced crowd safety research and repeatable experimentation.

EUMAS Conference 2017 Conference Paper

How Game Complexity Affects the Playing Behavior of Synthetic Agents

• Chairi Kiourt
• Dimitris Kalles
• Panagiotis Kanellopoulos

Abstract Agent based simulation of social organizations, via the investigation of agents’ training and learning tactics and strategies, has been inspired by the ability of humans to learn from social environments which are rich in agents, interactions and partial or hidden information. Such richness is a source of complexity that an effective learner has to be able to navigate. This paper focuses on the investigation of the impact of the environmental complexity on the game playing-and-learning behavior of synthetic agents. We demonstrate our approach using two independent turn-based zero-sum games as the basis of forming social events which are characterized both by competition and cooperation. The paper’s key highlight is that as the complexity of a social environment changes, an effective player has to adapt its learning and playing profile to maintain a given performance profile.

ECAI Conference 2016 Conference Paper

Data Set Operations to Hide Decision Tree Rules

• Dimitris Kalles
• Vassilios S. Verykios
• Georgios Feretzakis
• Athanassios Papagelis

This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. Our record augmentation approach for hiding sensitive classification rules in binary datasets is preferred over other heuristic solutions like output perturbation or cryptographic techniques since the raw data itself is readily available for public use. We describe the process and an indicative experiment using a prototype hiding tool.

EUMAS Conference 2015 Conference Paper

Human Rating Methods on Multi-agent Systems

• Chairi Kiourt
• Dimitris Kalles
• George Pavlidis

Abstract Modern artificial intelligence approaches study game-playing agents in multi-agent social environments, in order to better simulate the real world playing behaviors; these approaches have already produced promising results. In this paper we present the results of applying human rating systems for competitive games with social activity, to evaluate synthetic agents’ performance in multi-agent systems. The widely used Elo and Glicko rating systems are tested in large-scale synthetic multi-agent game-playing social events, and their rating outcome is presented and analyzed.

EUMAS Conference 2015 Conference Paper

Learning in Multi Agent Social Environments with Opponent Models

• Chairi Kiourt
• Dimitris Kalles

Abstract We examine how synthetic agents interact in social environments employing a variety of agent training strategies against diverse opponents. Such agent training and playing methods indicate that quality playing relies more on the correct set-up of the learning mechanism than on experience. The experimentation provides valuable insight into the potential of an agent to compete against other agents in its environment and yet manage to also co-operate so that this particular environment allows for the emergence of a competitive champion agent, which will represent its group in further contests. Additionally, by investigating performance while constraining the number of moves we gain interesting insight into competitive learning and playing with resource constraints.

ICML Conference 2001 Conference Paper

Breeding Decision Trees Using Evolutionary Techniques

• Athanassios Papagelis
• Dimitris Kalles