Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Game-theoretic solution concepts prescribe how rational parties should act, but to become
operational the concepts need to be accompanied by algorithms. I will review the state of …

Agents that interact with other agents often do not know a priori what the other agents'
strategies are, but have to maximise their own online return while interacting with and …

Poker is a challenging game with strong human and computer players. In this thesis, we will
explore four approaches towards creating a computer program capable of challenging these …

Reinforcement Learning (RL) is a way to train artificial agents to autonomously interact with
the world. In practice however, RL still has limitations that prohibit the deployment of RL …

Living organisms adapt to challenges through evolution. This has proven to be a key
difficulty in developing therapies, since the organisms evolve resistance. I propose the wild …

Intelligent agents, human or artificial, often change their behaviour as they interact with other
agents. For an agent to optimise its performance when interacting with such agents, it must …

Opponent models allow software agents to assess a multi-agent environment more
accurately and therefore improve the agent's performance. This paper makes use of coarse …