Generalist probability theory and dynamic decision support systems


Generalist probability theory and dynamic decision support systems – This paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.

We use the model for both action recognition and classification tasks. Unlike previous approaches, we do not require a large number of examples to learn the structure, and the structure is learned automatically. Therefore, it is natural to ask whether the structure of the task is more informative than the examples it is learning from. This paper proposes a new model based on the deep reinforcement learning method. The model is built with three layers: a layer in which an agent can control the environment, a layer in which an agent uses its actions and a layer called the hidden layer to represent the reward-value relationship between actions. The hidden layer is learned from the learned model through reinforcement learning, and the reward-value relationship between actions is learned by using the reinforcement learning techniques. An evaluation on the UCI dataset of 9,891 actions demonstrates the effectiveness of the model of learning from examples.

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Generalist probability theory and dynamic decision support systems

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  • Augment Auto-Associative Expression Learning for Identifying Classifiers with Overlapping Variables

    Adversarial Data Analysis in Multi-label ClassificationWe use the model for both action recognition and classification tasks. Unlike previous approaches, we do not require a large number of examples to learn the structure, and the structure is learned automatically. Therefore, it is natural to ask whether the structure of the task is more informative than the examples it is learning from. This paper proposes a new model based on the deep reinforcement learning method. The model is built with three layers: a layer in which an agent can control the environment, a layer in which an agent uses its actions and a layer called the hidden layer to represent the reward-value relationship between actions. The hidden layer is learned from the learned model through reinforcement learning, and the reward-value relationship between actions is learned by using the reinforcement learning techniques. An evaluation on the UCI dataset of 9,891 actions demonstrates the effectiveness of the model of learning from examples.


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