We are excited to announce that the SRSC workshop will be held jointly with another AAMAS workshop on Collaborative Agents Research & Development (CARE). The new website of the joint workshop is Joint Workshop on Autonomous Agents for Social Good (AASG).


Strategic reasoning is essential for modeling societal challenges in presence of adversaries, competitions, strategic interactions or uncertainties. Research and applications related to strategic reasoning span a broad variety of disciplines, including computer science, electrical engineering, economics, biology, political science, business, law, public policy, and many others. The focus of this workshop is to bring together the broad community working on Strategic Reasoning motivated by Societal Challenges.

One of the most successful examples in the past decade of strategic reasoning in multi-agent systems is its instantiation in security and public safety, a key societal challenges facing our world today. This has led to the development of security games which have been used to model many real-world security problems. Besides various lines of research developments, several software assistants rooted from these research results have been successfully deployed in real world, showing a high practical impact of such strategic reasoning. Examples include, but are not limited to, patrolling assistants for seaports and airports, scheduling air marshals, ticket audit in transit systems, fishery protection and prevention of illegal poaching. Recently, such models have increasing applicability in cyber, as well as cyber-physical system (CPS) security, such as adversarial machine learning methods (for use, for example, in intrusion detection systems), resilient sensor placement and monitoring strategies, and privacy preserving data publishing and auditing systems. This shows that applications of strategic reasoning are not only restricted to physical security – in fact, we believe that strategic reasoning is important in almost every field exhibiting adversaries, competitions or uncertainties.

While there has been significant progress, there still exist many major challenges facing the design of effective approaches as well as developing real-world applications to address societal challenges. These include building predictive behavioral models for the adversaries, machine learning for crime prediction, privacy control, voting manipulation, etc. Addressing these challenges requires collaboration among different communities including artificial intelligence, game theory, operations research, social science, and psychology.

The workshop will serve two purposes in this regard. First, the workshop will provide an opportunity to showcase real-world deployments of AI/MAS research. More often than not, unexpected practical challenges emerge when solutions developed in the lab are deployed in the real world, which makes it challenging to utilize complex and well thought out computational/modeling advances. Learning about the challenges faced in these deployments during the workshop will help us understand lessons of moving from the lab to the real world. Also, there is a need to build AI/MAS systems which dynamically adapt to changing environments, are robust to errors in execution and planning, and handle uncertainties of different kinds that are common in the real world. Addressing these challenges requires collaboration from different communities including artificial intelligence, game theory, operations research, social science, and psychology. This workshop is structured to encourage a lively exchange of ideas between members from these communities.