An agent must learn and adapt quickly when playing against other agents. This process is challenging in particular when playing in stochastic environments against other learning agents. In this paper, we introduce a fast and adaptive learning algorithm for repeated stochastic games (FAL-SG). FAL-SG utilizes lossy game abstraction to reduce the state space… (More)
The ability to learn and adapt when playing against an adap-tive opponent requires the ability to predict the opponent's behavior. Capturing any changes in the opponent's behavior during a sequence of plays is critical to achieve positive outcomes in such an environment. We identify two new requirements that we suggest are essential for agents that learn in… (More)
Models for predicting the risk of cardiovascular (CV) events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restrict the predictive power… (More)
We propose a novel algorithm that is able to learn and adapt to an opponent even within a limited number of interactions and against a rapidly adapting opponent. The context we use is two player normal form games. We compare the performance of an agent using our algorithm against agents using existing multiagent learning algorithms.