Prof. David Sarne

Intelligent Systems and Human-Agent Interaction

Intelligent systems working alongside humans are required not only to make accurate decisions but also to coordinate, cooperate, explain their actions, and build trust. These challenges arise in diverse contexts—from robots assisting humans to multi-agent systems and environments where information and interests evolve over time.

Prof. David Sarne’s research focuses on developing models, algorithms, and intelligent mechanisms for interaction, cooperation, and decision-making between people, autonomous agents, and robots. His work integrates AI and Game Theory, combining theoretical analysis with empirical experiments involving real users - both in laboratory settings and via online crowdworking platforms . Key applications include robotic systems for rehabilitation, gamified environments for social skills training, pattern recognition in Prediction Markets, and developing XAI methods to explain choices between competing answers, reasoning paths, or arguments in AI systems.

Primary Research Areas:

• Multi-Agent Systems: Mechanisms for competition, coordination, and collaborative search.

• Human-Agent Interaction: Strategic information disclosure and its impact on trust, satisfaction, and effectiveness.

• Explainable AI (XAI): Developing explanations for systems choosing between competing reasoning paths or arguments, including LLM-based systems and reasoning aggregation.

• Game Theory: Designing systems and decision-making processes in dynamic environments.

Research Nature:

 Theoretical-Applied, combining algorithmic development with empirical experiments and industrial collaboration.

Career Path:

 Graduates transition into advanced research and development roles globally:

• Academic Research: PhD and postdoctoral tracks at top-tier universities (e.g., Harvard, UT Austin).

• Research & Development (R&D): Research and algorithm design roles at leading tech companies.

• Intelligent Systems: Developing autonomous systems and AI-driven decision-making processes. The group’s training combines theoretical depth with empirical and applied experience.