112 research outputs found
Multi-Dimensional Causal Discovery
We propose a method for learning causal relations within high-dimensional tensor data as they are typically recorded in non-experimental databases. The method allows the simultaneous inclusion of numerous dimensions within the data analysis such as samples, time and domain variables construed as tensors. In such tensor data we exploit and integrate non-Gaussian models and tensor analytic algorithms in a novel way. We prove that we can determine simple causal relations independently of how complex the dimensionality of the data is. We rely on a statistical decomposition that flattens higher-dimensional data tensors into matrices. This decomposition preserves the causal information and is therefore suitable for structure learning of causal graphical models, where a causal relation can be generalised beyond dimension, for example, over all time points. Related methods either focus on a set of samples for instantaneous effects or look at one sample for effects at certain time points. We evaluate the resulting algorithm and discuss its performance both with synthetic and real-world data.
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
We present a novel negotiation model that allows an agent to learn how to
negotiate during concurrent bilateral negotiations in unknown and dynamic
e-markets. The agent uses an actor-critic architecture with model-free
reinforcement learning to learn a strategy expressed as a deep neural network.
We pre-train the strategy by supervision from synthetic market data, thereby
decreasing the exploration time required for learning during negotiation. As a
result, we can build automated agents for concurrent negotiations that can
adapt to different e-market settings without the need to be pre-programmed. Our
experimental evaluation shows that our deep reinforcement learning-based agents
outperform two existing well-known negotiation strategies in one-to-many
concurrent bilateral negotiations for a range of e-market settings
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