Modelling naturalistic Decision Making using Neural Networks

This thesis describes two studies conducted within a naturalistic decision making paradigm. Study One examines the choice of university for master level education. This decision is presented as a consequential choice decision task. Students, who had been offered placements at Cranfield University for the 1998/99 term, participated in this research. Factors influencing the participant’s decision to attend or not to attend Cranfield were collected with a questionnaire specifically designed for this purpose. The final data set contained 267 questionnaires. Study two describes a decision where a disruptive passenger threatens a hypothetical flight. Sixty-five professional members of flight crew participated in a series of semi-structured telephone interviews during which they described their decision-making process for dealing with this situation. This decision process is presented as a pattern-matching task. Artificial neural networks were used to model the decision on the basis of the input variables (questionnaire items in study one and interview variables in study two) undertaken to produce an empirically verifiable model of the participants decision making process. Cross-validation of the results showed that decision outcomes could be predicted on the basis of the models. The cross-validation results, in terms of classifications are compared with discriminant function analysis classification results, to determine if neural networks or discriminant function analysis is a more appropriate form of analysis for modelling a naturalistic decision. Both studies show that neural networks outperformed the discriminant function analysis results in terms of classification. Press’s Q analyses also support this finding. It is suggested that neural networks may be a viable way of modelling naturalistic decisions.MPhi

Controller decision making using neural networks

Most industrial processes contain nonlinearities, making them difficult to control. To overcome this issue many authors have developed complex nonlinear algorithms and models, most of them being process dependant. However, creating local models to approximate the plant by linear regions is a suitable approach in most cases. This approach lets the engineer create local PID controllers and switch them according to the plant linear regions.Master of Science (Computer Control and Automation

Decision Making for Self-Driving Vehicles in Unexpected Environments Using Efficient Reinforcement Learning Methods

Deep reinforcement learning (DRL) enables autonomous vehicles to perform complex decision making using neural networks. However, previous DRL networks only output decisions, so there is no way to determine whether the decision is proper. Reinforcement learning agents may continue to produce wrong decisions in unexpected environments not encountered during the learning process. In particular, one wrong decision can lead to an accident in autonomous driving. Therefore, it is necessary to indicate whether the action is a reasonable decision. As one such method, uncertainty can inform whether the agent’s decision is appropriate for practical application where safety must be guaranteed. Therefore, this paper provides uncertainty in the decision by proposing DeepSet-Q with Gaussian mixture (DwGM-Q), which converges the existing DeepSet-Q and mixture density network (MDN). Calculating uncertainty with the Gaussian mixture model (GMM) produced from MDN made it possible to calculate faster than the existing ensemble method. Moreover, it verified how the agent responds to the unlearned situation through the Simulation of Urban MObility (SUMO) simulator and compared the uncertainty of the decision between the learned and nontrained situation