Dynamic Systems as a Multi-Agent Simulation


Dynamic Systems as a Multi-Agent Simulation – The recent advances in AI applications have proven to be highly successful. In this paper, we present a system that uses a human-generated video from a mobile phone to perform complex tasks such as action recognition and vision in a robotic arm, as a semi-supervised process. We train the robot to perform multiple, sequential, action-based tasks, based on the action set that human players perform on the video. These tasks are presented as a new feature from the video, which could be used as a proxy to measure cognitive activity. The video captured by the robot shows human players performing multiple actions and actions on different video frames, in order to assess the visual state of the agent. We show how in this way the robotic arm and our video can be integrated to a single, sequential action detection system. In particular, we show how to train an action-tracking system that aims to recognize the actions of each player as a sequence of action clusters. We analyze the results of both the robot and human tasks to demonstrate the effectiveness of the system.

A large number of algorithms were used to predict the outcome of a trial. A special class of these algorithms named the Statistical Risk Minimization algorithm (SRM) was used to identify risk factors that can affect the outcome of a trial. In some cases, it was important to consider these risk factors before using these algorithms, since they could increase the quality of those risks. In this paper, we investigated how algorithms of the Statistical Risk Minimization (SRM) approach to risk prediction using the Random Forests algorithm was to reduce the quality of the outcomes of a trial. The results obtained showed that the random forest algorithm, which is a well-known algorithm for the problem of risk prediction, could decrease the quality of outcomes of trial by more than half compared to other algorithm.

Convex Tensor Decomposition with the Deterministic Kriging Distance

Ranking Forests using Neural Networks

Dynamic Systems as a Multi-Agent Simulation

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  • Evaluating Deep Predictive Models on Unlabeled Data for Detecting Drug-Drug Interaction

    The Data Science Approach to Empirical Risk MinimizationA large number of algorithms were used to predict the outcome of a trial. A special class of these algorithms named the Statistical Risk Minimization algorithm (SRM) was used to identify risk factors that can affect the outcome of a trial. In some cases, it was important to consider these risk factors before using these algorithms, since they could increase the quality of those risks. In this paper, we investigated how algorithms of the Statistical Risk Minimization (SRM) approach to risk prediction using the Random Forests algorithm was to reduce the quality of the outcomes of a trial. The results obtained showed that the random forest algorithm, which is a well-known algorithm for the problem of risk prediction, could decrease the quality of outcomes of trial by more than half compared to other algorithm.


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