Learn More
The single-agent multi-armed bandit problem can be solved by an agent that learns the values of each action using reinforcement learning. However, the multi-agent version of the problem, the iterated normal form game, presents a more complex challenge, since the rewards available to each agent depend on the strategies of the others. We consider the behavior(More)
A general class of adaptive process in games is developed, which significantly gen-eralises weakened fictitious play (Van der Genugten, 2000) and includes several interesting fictitious-play-like processes as special cases. The general model is rigorously analysed using the best response differential inclusion, and shown to converge in games with the(More)
OBJECTIVES To examine clinical outcomes of an interdisciplinary day-hospital treatment program (comprised of physical, occupational, and cognitive-behavioral therapies with medical and nursing services) for pediatric complex regional pain syndrome (CRPS). METHODS The study is a longitudinal case series of consecutive patients treated in a day-hospital(More)
In every sequential decision problem in an unknown environment, the decision maker faces a dilemma over whether to explore to discover more about the environment, or to exploit current knowledge. We address the exploration/exploitation dilemma in a general setting encompassing both standard and contextualised bandit problems. In this article we extend an(More)
We consider reinforcement learning algorithms in normal form games. Using two-timescales stochastic approximation, we introduce a model-free algorithm which is asymptotically equivalent to the smooth fictitious play algorithm, in that both result in asymptotic pseudotrajectories to the flow defined by the smooth best response dynamics. Both of these(More)
Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus(More)
ALADDIN [1] is a multidisciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between(More)
Functional imaging has revolutionized the neurosciences. In the pain field it has dramatically altered our understanding of how the brain undergoes significant functional, anatomical and chemical changes in patients with chronic pain. However, most studies have been performed in adults. Because functional imaging is non-invasive and can be performed in(More)
Distributed constraint optimisation problems (DCOPs) are important in many areas of computer science and optimisation. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximising a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus(More)