Strategy Logic
Strategy logic focuses on formalizing and analyzing strategic reasoning in multi-agent systems, aiming to model and predict the outcomes of interactions where agents pursue their own objectives. Current research emphasizes applying and extending these frameworks to diverse domains, including game playing (using LLMs and other models), robotics control (e.g., via deep reinforcement learning), and cybersecurity, often focusing on improving the efficiency and robustness of algorithms. This field is significant for its potential to improve the design of autonomous systems, enhance the understanding of complex interactions, and provide rigorous tools for evaluating the strategic capabilities of AI agents.
Papers
A Strategy for Implementing description Temporal Dynamic Algorithms in Dynamic Knowledge Graphs by SPIN
Alireza Shahbazi, Seyyed Ahmad Mirsanei, Malikeh Haj Khan Mirzaye Sarraf, Behrouz Minaei Bidgoli
Challenge design roadmap
Hugo Jair Escalante Balderas, Isabelle Guyon, Addison Howard, Walter Reade, Sebastien Treguer
Strategic Abilities of Forgetful Agents in Stochastic Environments
Francesco Belardinelli, Wojciech Jamroga, Munyque Mittelmann, Aniello Murano
Scalable Verification of Strategy Logic through Three-valued Abstraction
Francesco Belardinelli, Angelo Ferrando, Wojciech Jamroga, Vadim Malvone, Aniello Murano