|Website:||website containing additional information|
|Period:||period 4 (week 17 through 26, i.e., 22-4-2019 through 28-6-2019; retake week 28)
|Participants:||up till now 84 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|week: 21||Thu 23-5-2019||8.30-10.30 uur||room: EDUC-BETA|
|week: 26||Thu 27-6-2019||8.30-10.30 uur||room: EDUC-GAMMA|
|week: 28||Thu 11-7-2019||8.30-10.30 uur||room: BBG-023||retake exam|
|Contents:||This course discusses algorithms for machine learning that typically occur in multi-agent systems, such as: reinforcement learning, no-regret learning, fictitious play, satisficing play, Bayesian learning, learning and teaching, and evolutionary learning (replicator dynamic).
The course assumes knowledge of probability theory and game theory. Knowledge of game theory can be acquired through the multi-agent systems course in the Utrecht AI master.
|Literature:||Available through the course site.
Download and print of material is a responsibility of the student.|
|Exam form:||Exam 1 (50%), exam 2 (50%).|
|Minimum effort to qualify for 2nd chance exam:||Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.|
|Description:||Multi-agent learning (MAL) studies agents that learn and adapt to the behaviour of other agents that themselves learn and adapt. The presence of other learning agents complicates learning, which makes the environment non-stationary (a situation of learning a moving target) and non-Markovian (a situation where not only experiences from the immediate past but also earlier experiences are relevant). It becomes less beneficent to only adapt to the behaviour of other agents, on the pain of being exploited by more steadfast agents that do not follow but instead impose their strategy on others. Important topics of MAL include (evolutionary) game theory, fictitious play, gradient dynamics, no-regret learning, multi-agent reinforcement learning (MinMax-Q, Nash-Q), leader (teacher) vs. follower (learner) adaptation, and the emergence of social conventions. Examples of domains that need robust MAL algorithms include manufacturing systems (managers of a factory coordinate to maximise their profit), distributed sensor networks (multiple sensors collaborate to perform a large-scale sensing task under strict power constraints), robo-soccer, disaster rescue (robots must safely find victims as fast as possible after an earthquake) and recreational games of imperfect information such as poker. Indeed, poker and simplified forms of poker are an important topics of research in multi-agent learning.|