Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Sciences (1329)
- Artificial Intelligence and Robotics (515)
- Engineering (356)
- Computer Engineering (169)
- Data Science (148)
-
- Social and Behavioral Sciences (143)
- Electrical and Computer Engineering (139)
- Statistics and Probability (129)
- Medicine and Health Sciences (117)
- Life Sciences (102)
- Databases and Information Systems (100)
- Earth Sciences (79)
- Theory and Algorithms (74)
- Mathematics (72)
- Physics (70)
- Environmental Sciences (69)
- Information Security (69)
- Numerical Analysis and Scientific Computing (69)
- Software Engineering (68)
- Other Computer Sciences (64)
- Business (58)
- Applied Mathematics (51)
- Arts and Humanities (45)
- Education (40)
- Medical Specialties (36)
- Chemistry (34)
- Applied Statistics (32)
- Operations Research, Systems Engineering and Industrial Engineering (32)
- Oceanography and Atmospheric Sciences and Meteorology (30)
- Institution
-
- Old Dominion University (115)
- Singapore Management University (104)
- Brigham Young University (74)
- Air Force Institute of Technology (66)
- TÜBİTAK (61)
-
- Zayed University (48)
- University of Texas at Arlington (44)
- New Jersey Institute of Technology (42)
- Technological University Dublin (40)
- Portland State University (38)
- University of Nebraska - Lincoln (38)
- Edith Cowan University (30)
- Western University (30)
- Chapman University (27)
- San Jose State University (26)
- City University of New York (CUNY) (25)
- University of Kentucky (25)
- University of South Florida (24)
- Boise State University (21)
- Utah State University (21)
- Louisiana State University (19)
- University of Texas Rio Grande Valley (19)
- University at Albany, State University of New York (18)
- University of Louisville (18)
- Wright State University (18)
- Southern Methodist University (17)
- University of Nevada, Las Vegas (17)
- University of Tennessee, Knoxville (17)
- California Polytechnic State University, San Luis Obispo (16)
- Dartmouth College (16)
- Publication Year
- Publication
-
- Theses and Dissertations (152)
- Research Collection School Of Computing and Information Systems (86)
- Electronic Theses and Dissertations (65)
- Turkish Journal of Electrical Engineering and Computer Sciences (60)
- Dissertations (51)
-
- All Works (48)
- Faculty Publications (40)
- Computer Science and Engineering Dissertations (24)
- Electrical & Computer Engineering Faculty Publications (24)
- Electronic Thesis and Dissertation Repository (23)
- Dissertations and Theses (22)
- Master's Projects (21)
- Conference papers (20)
- Doctoral Dissertations (20)
- Computer Science Faculty Publications (19)
- Computer Science and Engineering Theses (19)
- Legacy Theses & Dissertations (2009 - 2024) (18)
- Articles (17)
- USF Tampa Graduate Theses and Dissertations (17)
- Master's Theses (16)
- Browse all Theses and Dissertations (15)
- Research outputs 2022 to 2026 (15)
- SMU Data Science Review (15)
- Boise State University Theses and Dissertations (14)
- Dissertations, Theses, and Capstone Projects (13)
- LSU Doctoral Dissertations (13)
- Mathematics, Physics, and Computer Science Faculty Articles and Research (13)
- CCE Theses and Dissertations (12)
- Honors Theses (12)
- Journal of System Simulation (12)
- Publication Type
Articles 1621 - 1650 of 1686
Full-Text Articles in Physical Sciences and Mathematics
Bootstrapping Events And Relations From Text, Ting Liu
Bootstrapping Events And Relations From Text, Ting Liu
Legacy Theses & Dissertations (2009 - 2024)
Information Extraction (IE) is a technique for automatically extracting structured data from text documents. One of the key analytical tasks is extraction of important and relevant information from textual sources. While information is plentiful and readily available, from the Internet, news services, media, etc., extracting the critical nuggets that matter to business or to national security is a cognitively demanding and time consuming task. Intelligence and business analysts spend many hours poring over endless streams of text documents pulling out reference to entities of interest (people, locations, organizations) as well as their relationships as reported in text. Such extracted "information …
A Survey Of Transfer Learning Methods For Reinforcement Learning, Nicholas Bone
A Survey Of Transfer Learning Methods For Reinforcement Learning, Nicholas Bone
Computer Science Graduate and Undergraduate Student Scholarship
Transfer Learning (TL) is the branch of Machine Learning concerned with improving performance on a target task by leveraging knowledge from a related (and usually already learned) source task. TL is potentially applicable to any learning task, but in this survey we consider TL in a Reinforcement Learning (RL) context. TL is inspired by psychology; humans constantly apply previous knowledge to new tasks, but such transfer has traditionally been very difficult for—or ignored by—machine learning applications. The goals of TL are to facilitate faster and better learning of new tasks by applying past experience where appropriate, and to enable autonomous …
Real-Time Automatic Price Prediction For Ebay Online Trading, Ilya Igorevitch Raykhel
Real-Time Automatic Price Prediction For Ebay Online Trading, Ilya Igorevitch Raykhel
Theses and Dissertations
While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; …
Development Of A Workflow For The Comparison Of Classification Techniques, Zanifa Omary
Development Of A Workflow For The Comparison Of Classification Techniques, Zanifa Omary
Masters
As the interest in machine learning and data mining springs up, the problem of how to assess learning algorithms and compare classifiers become more pressing. This has been associated with the lack of comprehensive and complete workflow depending on the project scale to provide guidance to its users. This means the success or failure of the project can be highly dependent on the person or team carrying it. The standard practice adopted by many researchers and experimenters has been to follow steps or phases from existing workflows such as CRISP-DM, KDD and SASSEMMA. However, as machine learning and data mining …
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson
Faculty Publications
This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. This paper describes two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ant colony system algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions …
Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Assessing The Costs Of Sampling Methods In Active Learning For Annotation, James Carroll, Robbie Haertel, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Faculty Publications
Traditional Active Learning (AL) techniques assume that the annotation of each datum costs the same. This is not the case when annotating sequences; some sequences will take longer than others. We show that the AL technique which performs best depends on how cost is measured. Applying an hourly cost model based on the results of an annotation user study, we approximate the amount of time necessary to annotate a given sentence. This model allows us to evaluate the effectiveness of AL sampling methods in terms of time spent in annotation. We acheive a 77% reduction in hours from a random …
Comparison Of Machine Learning Algorithms For Modeling Species Distributions: Application To Stream Invertebrates From Western Usa Reference Sites, Margi Dubal
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Machine learning algorithms are increasingly being used by ecologists to model and predict the distributions of individual species and entire assemblages of sites. Accurate prediction of distribution of species is an important factor in any modeling. We compared prediction accuracy of four machine learning algorithms-random forests, classification trees, support vector machines, and gradient boosting machines to a traditional method, linear discriminant models (LDM), on a large set of stream invertebrate data collected at 728 reference sites in the western United States. Classifications were constructed for individual species and for assemblages of sites clustered a priori by similarity on biological characteristics. …
Machine Learning And Graph Theory Approaches For Classification And Prediction Of Protein Structure, Gulsah Altun
Machine Learning And Graph Theory Approaches For Classification And Prediction Of Protein Structure, Gulsah Altun
Computer Science Dissertations
Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this …
Improving Liquid State Machines Through Iterative Refinement Of The Reservoir, R David Norton
Improving Liquid State Machines Through Iterative Refinement Of The Reservoir, R David Norton
Theses and Dissertations
Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification …
Learning Policies For Embodied Virtual Agents Through Demonstration, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura
Learning Policies For Embodied Virtual Agents Through Demonstration, Jonathan Dinerstein, Parris K. Egbert, Dan A. Ventura
Faculty Publications
Although many powerful AI and machine learning techniques exist, it remains difficult to quickly create AI for embodied virtual agents that produces visually lifelike behavior. This is important for applications (e.g., games, simulators, interactive displays) where an agent must behave in a manner that appears human-like. We present a novel technique for learning reactive policies that mimic demonstrated human behavior. The user demonstrates the desired behavior by dictating the agent’s actions during an interactive animation. Later, when the agent is to behave autonomously, the recorded data is generalized to form a continuous state-to-action mapping. Combined with an appropriate animation algorithm …
A Direct Algorithm For The K-Nearest-Neighbor Classifier Via Local Warping Of The Distance Metric, Tohkoon Neo
A Direct Algorithm For The K-Nearest-Neighbor Classifier Via Local Warping Of The Distance Metric, Tohkoon Neo
Theses and Dissertations
The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is therefore desirable for increasing the accuracy of these condensed models. Unfortunately, there does not exist a boosting algorithm that works well with k-NN directly. We present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting. An empirical study conducted …
Learning Multiple Languages In Groups, Sanjay Jain, Efim Kinber
Learning Multiple Languages In Groups, Sanjay Jain, Efim Kinber
School of Computer Science & Engineering Faculty Publications
We consider a variant of Gold’s learning paradigm where a learner receives as input different languages (in the form of one text where all input languages are interleaved). Our goal is to explore the situation when a more “coarse” classification of input languages is possible, whereas more refined classification is not. More specifically, we answer the following question: under which conditions, a learner, being fed different languages, can produce grammars covering all input languages, but cannot produce grammars covering input languages for any . We also consider a variant of this task, where each of the output grammars may not …
Context-Aware Statistical Debugging: From Bug Predictors To Faulty Control Flow Paths, Lingxiao Jiang, Zhendong Su
Context-Aware Statistical Debugging: From Bug Predictors To Faulty Control Flow Paths, Lingxiao Jiang, Zhendong Su
Research Collection School Of Computing and Information Systems
Effective bug localization is important for realizing automated debugging. One attractive approach is to apply statistical techniques on a collection of evaluation profiles of program properties to help localize bugs. Previous research has proposed various specialized techniques to isolate certain program predicates as bug predictors. However, because many bugs may not be directly associated with these predicates, these techniques are often ineffective in localizing bugs. Relevant control flow paths that may contain bug locations are more informative than stand-alone predicates for discovering and understanding bugs. In this paper, we propose an approach to automatically generate such faulty control flow paths …
Heuristic Weighted Voting, Kristine Perry Monteith
Heuristic Weighted Voting, Kristine Perry Monteith
Theses and Dissertations
Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble results in an overall average increase in …
A Data-Dependent Distance Measure For Transductive Instance-Based Learning, Jared Lundell, Dan A. Ventura
A Data-Dependent Distance Measure For Transductive Instance-Based Learning, Jared Lundell, Dan A. Ventura
Faculty Publications
We consider learning in a transductive setting using instance-based learning (k-NN) and present a method for constructing a data-dependent distance “metric” using both labeled training data as well as available unlabeled data (that is to be classified by the model). This new data-driven measure of distance is empirically studied in the context of various instance-based models and is shown to reduce error (compared to traditional models) under certain learning conditions. Generalizations and improvements are suggested.
Limitations And Extensions Of The Wolf-Phc Algorithm, Philip R. Cook
Limitations And Extensions Of The Wolf-Phc Algorithm, Philip R. Cook
Theses and Dissertations
Policy Hill Climbing (PHC) is a reinforcement learning algorithm that extends Q-learning to learn probabilistic policies for multi-agent games. WoLF-PHC extends PHC with the "win or learn fast" principle. A proof that PHC will diverge in self-play when playing Shapley's game is given, and WoLF-PHC is shown empirically to diverge as well. Various WoLF-PHC based modifications were created, evaluated, and compared in an attempt to obtain convergence to the single shot Nash equilibrium when playing Shapley's game in self-play without using more information than WoLF-PHC uses. Partial Commitment WoLF-PHC (PCWoLF-PHC), which performs best on Shapley's game, is tested on other …
Improving Neural Network Classification Training, Michael Edwin Rimer
Improving Neural Network Classification Training, Michael Edwin Rimer
Theses and Dissertations
The following work presents a new set of general methods for improving neural network accuracy on classification tasks, grouped under the label of classification-based methods. The central theme of these approaches is to provide problem representations and error functions that more directly improve classification accuracy than conventional learning and error functions. The CB1 algorithm attempts to maximize classification accuracy by selectively backpropagating error only on misclassified training patterns. CB2 incorporates a sliding error threshold to the CB1 algorithm, interpolating between the behavior of CB1 and standard error backpropagation as training progresses in order to avoid prematurely saturated network weights. CB3 …
Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas
Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas
Theses and Dissertations
Ant colony optimization algorithms have long been touted as providing an effective and efficient means of generating high quality solutions to NP-hard optimization problems. Unfortunately, while the structure of the algorithm is easy to parallelize, the nature and amount of communication required for parallel execution has meant that parallel implementations developed suffer from decreased solution quality, slower runtime performance, or both. This thesis explores a new strategy for ant colony parallelization that involves Area of Expertise (AOE) learning. The AOE concept is based on the idea that individual agents tend to gain knowledge of different areas of the search space …
Solar Activity Detection And Prediction Using Image Processing And Machine Learning Techniques, Gang Fu
Solar Activity Detection And Prediction Using Image Processing And Machine Learning Techniques, Gang Fu
Dissertations
The objective of the research in this dissertation is to develop the methods for automatic detection and prediction of solar activities, including prominence eruptions, emerging flux regions and solar flares. Image processing and machine learning techniques are applied in this study. These methods can be used for automatic observation of solar activities and prediction of space weather that may have great influence on the near earth environment.
The research presented in this dissertation covers the following topics: i) automatic detection of prominence eruptions (PBs), ii) automatic detection of emerging flux regions (EFRs), and iii) automatic prediction of solar flares.
In …
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Research Collection School Of Computing and Information Systems
Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees. Our experiments show that all these classifiers are comparable in terms of accuracy, while Bayesian networks have the additional advantage of being able to provide insights into the relationships among the variables. …
Obstacle Avoidance And Path Traversal Using Interactive Machine Learning, Jonathan M. Turner
Obstacle Avoidance And Path Traversal Using Interactive Machine Learning, Jonathan M. Turner
Theses and Dissertations
Recently there has been a growing interest in using robots in activities that are dangerous or cost prohibitive for humans to do. Such activities include military uses and space exploration. While robotic hardware is often capable of being used in these types of situations, the ability of human operators to control robots in an effective manner is often limited. This deficiency is often related to the control interface of the robot and the level of autonomy that control system affords the human operator. This thesis describes a robot control system, called the safe/unsafe system, which gives a human operator the …
Cognitive And Behavioral Model Ensembles For Autonomous Virtual Characters, Jeffrey S. Whiting
Cognitive And Behavioral Model Ensembles For Autonomous Virtual Characters, Jeffrey S. Whiting
Theses and Dissertations
Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's …
Active Learning For Part-Of-Speech Tagging: Accelerating Corpus Annotation, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Active Learning For Part-Of-Speech Tagging: Accelerating Corpus Annotation, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Faculty Publications
In the construction of a part-of-speech annotated corpus, we are constrained by a fixed budget. A fully annotated corpus is required, but we can afford to label only a subset. We train a Maximum Entropy Markov Model tagger from a labeled subset and automatically tag the remainder. This paper addresses the question of where to focus our manual tagging efforts in order to deliver an annotation of highest quality. In this context, we find that active learning is always helpful. We focus on Query by Uncertainty (QBU) and Query by Committee (QBC) and report on experiments with several baselines and …
Evolutionary Granular Kernel Machines, Bo Jin
Evolutionary Granular Kernel Machines, Bo Jin
Computer Science Dissertations
Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. …
Learning To Classify E-Mail, Irena Koprinska, Josiah Poon, James Clark, Jason Yuk Hin Chan
Learning To Classify E-Mail, Irena Koprinska, Josiah Poon, James Clark, Jason Yuk Hin Chan
Research Collection School Of Computing and Information Systems
In this paper we study supervised and semi-supervised classification of e-mails. We consider two tasks: filing e-mails into folders and spam e-mail filtering. Firstly, in a supervised learning setting, we investigate the use of random forest for automatic e-mail filing into folders and spam e-mail filtering. We show that random forest is a good choice for these tasks as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as decision trees, support vector machines and naive Bayes. We introduce a new accurate feature selector with linear time complexity. …
Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo
Towards A Self-Calibrating Video Camera Network For Content Analysis And Forensics, Imran Junejo
Electronic Theses and Dissertations
Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how …
Knowledge-Based Methods For Automatic Extraction Of Domain-Specific Ontologies, Janardhana R. Punuru
Knowledge-Based Methods For Automatic Extraction Of Domain-Specific Ontologies, Janardhana R. Punuru
LSU Doctoral Dissertations
Semantic web technology aims at developing methodologies for representing large amount of knowledge in web accessible form. The semantics of knowledge should be easy to interpret and understand by computer programs, so that sharing and utilizing knowledge across the Web would be possible. Domain specific ontologies form the basis for knowledge representation in the semantic web. Research on automated development of ontologies from texts has become increasingly important because manual construction of ontologies is labor intensive and costly, and, at the same time, large amount of texts for individual domains is already available in electronic form. However, automatic extraction of …
Using Machine Learning Techniques To Create Ai Controlled Players For Video Games, Bhuman Soni
Using Machine Learning Techniques To Create Ai Controlled Players For Video Games, Bhuman Soni
Theses : Honours
This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative …
Learning In Short-Time Horizons With Measurable Costs, Patrick Bowen Mullen
Learning In Short-Time Horizons With Measurable Costs, Patrick Bowen Mullen
Theses and Dissertations
Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to …
A Decentralized Reinforcement Learning Controller For Collaborative Driving, Luke Ng, Christopher M. Clark, Jan P. Huissoon
A Decentralized Reinforcement Learning Controller For Collaborative Driving, Luke Ng, Christopher M. Clark, Jan P. Huissoon
Computer Science and Software Engineering
Research in the collaborative driving domain strives to create control systems that coordinate the motion of multiple vehicles in order to navigate traffic both efficiently and safely. In this paper a novel individual vehicle controller based on reinforcement learning is introduced. This controller is capable of both lateral and longitudinal control while driving in a multi-vehicle platoon. The design and development of this controller is discussed in detail and simulation results showing learning progress and performance are presented.