Accountable Multi-Agent Sequential Decision Making
Stelios Triantafyllou
Max Planck Institute for Software Systems
30 Oct 2025, 11:00 am - 12:00 pm
Saarbrücken building E1 5, room 029
SWS Student Defense Talks - Thesis Proposal
As AI agents increasingly engage in high-stakes decision
making, it is essential to assess their accountability in ways that are
both fair and interpretable. This involves explaining expected or
realized outcomes of multi-agent systems and attributing responsibility
for those outcomes to the participating agents. Addressing these
challenges is key to fostering societal trust and easing the adoption of
AI decision makers. This thesis investigates accountability in
multi-agent sequential decision making. We develop methods to attribute
responsibility for observed outcomes and overall system performance, ...
As AI agents increasingly engage in high-stakes decision
making, it is essential to assess their accountability in ways that are
both fair and interpretable. This involves explaining expected or
realized outcomes of multi-agent systems and attributing responsibility
for those outcomes to the participating agents. Addressing these
challenges is key to fostering societal trust and easing the adoption of
AI decision makers. This thesis investigates accountability in
multi-agent sequential decision making. We develop methods to attribute
responsibility for observed outcomes and overall system performance,
design efficient approximation algorithms for otherwise intractable
attribution problems, and introduce causal tools to explain how agents’
decisions influence outcomes. Together, these contributions establish
theoretical foundations and practical tools for accountable decision
making, drawing on and integrating insights from causality, multi-agent
reinforcement learning and game theory.
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