We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If $X$ satisfies (condition $A$ AND condition $B$) OR (condition $C$) OR $\cdots$, then $Y=1$. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets -- BRS) to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.

A Rule Set model consists of a small number of short rules for interpretable classification, where an instance is classified as positive if it satisfies at least one of the rules. The rule set provides reasons for predictions, and also descriptions of a particular class. We present a Bayesian framework for learning Rule Set models, with prior parameters that the user can set to encourage the model to have a desired size and shape in order to conform with a domain-specific definition of interpretability. We use an efficient inference approach for searching for the MAP solution and provide theoretical bounds to reduce computation. We apply Rule Set models to ten UCI data sets and compare the performance with other interpretable and non-interpretable models.

SmartDeviceLink (SDL) is an open-source software development kit (SDK) that enables a smart-device to connect to the vehicle, providing functions for safe and easy access to the vehicle human-machine interface (HMI) and the ability to programmatically control vehicle functions. This paper discusses a framework for developing intelligent control applications that implement personalized and context-aware features for automotive climate control systems. There is also a discussion of integration of wearables, internet-of-things (IoT) sensors, cloud and mobile machine learning.

This paper presents our research in building a virtual humidity sensor using recurrent Neural Networks. Recurrent Neural Networks are promising methods for the prediction of time series because they provide feedback connections from hidden layer to its inputs and, therefore, can store temporal information learned from previous time steps. This study applies Elman Recurrent Neural Network (ERNN) to forecast the specific humidity from three weather stations. In addition, this study examines the feasibility of applying ERNN in time series forecasting by comparing it with multilayer perceptron network. The experiment results indicate that ERNN is a promising alternative to specific humidity forecasting.

Dynamic traffic management methods constitute the intelligent core of Intelligent Transportation Systems (ITS). In order for these methods to be effective and deployable in real-time, there is a need to develop models that predict future traffic conditions in a computational time much less than real time. In this paper we report on parallel implementations for a class of Dynamic Traffic Assignment (DTA) models, known as macroscopic DTA models. This class of models possess mathematical formulations which are solved using various algorithms. Two parallel decomposition strategies based on network topology and time are investigated and implemented in a distributed memory environment. Numerical results show that for the network topology based decomposition strategy, a speed-up of 5 is observed when the number of processors is 10 and the asymptotic speed-up is about 10. For the time-based decomposition strategy a speed-up of 6.5 is observed when the number of processors is 10 and the asymptotic speed-up is about 25.

In this paper, a CAD-guided robot path generator is developed for the spray painting of compound surfaces commonly seen in automotive manufacturing. Instead of widely used parametric representation of surfaces, a planar facet scheme is used to approximate the painting surfaces. As a result, a new combinatorial gun path planning algorithm is proposed. In this algorithm, big patches are formed by stitching small surfaces together. Therefore, the path planning is solved based on the global characteristics of tire part, and the resulting spray gun paths are well-behaved in the sense of time, coverage, and wastage. The proposed algorithm has been implemented and tested using ROBCAD/sup TM//Paint.