Multiagent learning is a key problem in AI. In the presence of multiple Nash equilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exaccerbated if the agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two interrelated problems: identifying the game and lear...

In this paper we present a methodology for describing adaptive educational-game environments and a model that supports the environment design process. These environments combine the advantages of educational games with those derived from the adaptation. The proposed methodology allows the specification of educational methods that can be used for the game environment generation. The educational ...

This research deals with learning in digital environments. Adaptive game-based learning considers learners’ preferred learning behavior and adapts the game system accordingly. In order to analyze learning behavior, the focus lies on a specific cognitive style, impulsive and reflective (I/R), which describe behaviors in problem-solving and decision-making environments. Impulsive people tend to r...

My research involves creating and evaluating adaptive game environments using player models created with data-driven techniques and algorithms. I hypothesize that I will be able to change parts of a game to elicit certain behaviors from players, and that these changes will also result in an increase of engagement and/or intrinsic motivation. Initial results in my testbed game, Scrabblesque, ind...

The replicator equation is the first and most important game dynamics studied in connection with evolutionary game theory. It was originally developed for symmetric games with finitely many strategies. Properties of these dynamics are briefly summarized for this case, including the convergence to and stability of the Nash equilibria and evolutionarily stable strategies. The theory is then exten...

We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among learning agents can improve when agents use different learning parameters or even evolve their learning parameters. Better coordination leads to less resources being wasted and agents achiev...

In this paper, we develop a new distributed adaptive power control framework for multi-cell OFDM systems based on the game theory. A specific utility function is defined considering the users’ achieved average utility per power, i.e., power unit based utility. We solve the subcarrier allocation issue naturally as well as the power control. Each user tries to maximize its utility by adjusting th...