H.3. Artificial Intelligence
M. Taghian; A. Asadi; R. Safabakhsh
Abstract
The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved ...
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The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.
H.3.2.6. Games and infotainment
A. Torkaman; R. Safabakhsh
Abstract
Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent ...
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Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope with the uncertainty existing in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-logs dataset; so it does not require a human expert’s knowledge. We evaluate our model on StarCraft which is considered as a unified test-bed in this domain. The model is compared with that proposed by Synnaeve and Bessiere. Experimental results on recorded games of human players show that the proposed model can predict the opponent’s future decisions more effectively. Using this model, it is possible to create an adaptive game intelligence algorithm applicable to RTS games, where the concept of build order (the order of building construction) exists.
