Learning, Inference and Control of Multi-Agent Systems
March 26-28, 2018
Spring Symposium Series, Stanford University, Palo Alto, California, USA
Paper Submission Deadline has been extended until November 6, 2017
We live in a multi-agent world and to be successful in that world, agents, and in particular, artificially intelligent agents, will need to learn to take into account the agency of others. They will need to compete in market places, cooperate in teams, communicate with others, coordinate their plans, and negotiate outcomes. Examples include self-driving cars interacting in traffic, personal assistants acting on behalf of humans and negotiating with other agents, swarms of unmanned aerial vehicles, financial trading systems, robotic teams, and household robots.
Furthermore, the evolution of human intelligence itself presumably depended on interaction among human agents, possibly starting out with confrontational scavenging  and culminating in the evolution of culture, societies, and language. Learning from other agents is a key feature of human intelligence and an important field of research in machine learning . It is therefore conceivable that exposing learning AI agents to multi-agent situations is necessary for their development towards intelligence.
We can also think of multi-agent systems as a design philosophy for complex systems. We can analyse complex systems in terms of agents at multiple scales. For example, we can view the system of world politics as an interaction of nation state agents, nation states as an interaction of organizations, and further down into departments, people etc. Conversely, when designing systems we can think of agents as building blocks or modules interacting to produce the behaviour of the system, e.g. .
Multi-agent systems can have desirable properties such as robustness and scalability, but their design requires careful consideration of incentive structures, learning, and communication. In the most extreme case, agents with individual views of the world, individual actuators, and individual incentive structures need to coordinate to achieve a common goal. To succeed they may need a Theory of Mind that allows them to reason about other agents’ intentions, beliefs, and behaviours . When multiple learning agents are interacting, the learning problem from each agent’s perspective may become non-stationary, non-Markovian, and only partially observable. Studying the dynamics of learning algorithms could lead to better insight about the evolution and stability of such systems .
Problems involving competing or cooperating agents feature in recent AI breakthroughs in competitive games [6,7], current ambitions of AI such as robotic football teams , and new research into emergent language and agent communication in reinforcement learning [9,10].
In summary, multi-agent learning will be of crucial importance to the future of computational intelligence and pose difficult and fascinating problems that need to be addressed across disciplines. The paradigm shift from single-agent to multi-agent systems will be pervasive and will require efforts across different fields including machine learning, cognitive science, robotics, natural computing, and (evolutionary) game theory. In this workshop we aim to bring together researchers from these different fields to discuss the current state of the art, future avenues and visions for work regarding theory and practice of multi-agent learning, inference, and decision-making.
 ‘Confrontational scavenging as a possible source for language and cooperation’, Derek Bickerton and Eörs Szathmáry, BMC Evolutionary Biology 2011
 ‘Apprenticeship Learning via Inverse Reinforcement Learning’, Pieter Abbeel and Andrew Y. Ng, ICML 2004
 ‘The Society of Mind’, Marvin Minsky, 1986
 ‘Building Machines That Learn and Think Like People’, Brenden M. Lake et al., CBMM Memo 2016
 ‘Evolutionary Dynamics of Multi-Agent Learning: A Survey’, Daan Bloembergen et al., JAIR 2015
 ‘Mastering the game of Go with deep neural networks and tree search’, David Silver et al., Nature 2016
 ‘Heads-up limit hold’em poker is solved’, Michael Bowling et al., Science 2015
 RoboCup, http://www.robocup.org/
 ‘Learning to Communicate with Deep Multi-Agent Reinforcement Learning’, Jakob N. Foerster et al., Arxiv 2016
 ‘Learning Multiagent Communication with Backpropagation’, Sainbayar Sukhbaatar et al. Arxiv 2016