Source: https://mentis.uta.edu/explore/profile/manfred-huber
Timestamp: 2019-04-19 01:21:54+00:00

Document:
M. Aurangzeb, F. Lewis, and M. Huber. "Efficient, Swarm-Based Path Finding in Unknown Graphs Using Reinforcement Learning". In Journal of Control and Intelligent Systems, Vol 3, pp 2583-2590, 2014.
H. Janzadeh and M. Huber. "Learning Policies in Partially Observable MDPs with Abstract Actions Using Value Iteration", To appear in Proceedings of the 26th International FLAIRS Conference (FLAIRS'13), St. Pete Beach, May 2013.
M. Aurangzeb, F.L. Lewis, M. Huber. "Efficient, Swarm-Based Path Finding in Unknown Graphs Using Reinforcement Learning", In Proceedings of the 10th IEEE International Conference on Control & Automation (ICCA 2013), Hangzhou, China, 2013.
H. Rahmanian, M. Huber. "Data Modeling Using Channel-Remapped Generalized Features", In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'13), Manchester, England, October 2013.
Djurdjevic P, Huber M (2013), Deep Belief Network for Modeling Hierarchical Reinforcement Learning Policies, To appear in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'13), Manchester, England, October 2013.
R. Su, T.M. Dockins, and M. Huber. "ICA Analysis of Face Color for Health Applications", To appear in Proceedings of the 26th International FLAIRS Conference (FLAIRS'13), St. Pete Beach, FL, 2013.
R. Fakoor, F. Ladhak, A. Nazi, M. Huber. "Using Deep Learning to Enhance Cancer Diagnosis and Classification". In Proceedings of the ICML Workshop on the Role of Machine Learning in Transforming Healthcare (WHEALTH). Atlanta, GA, June 2013.
T. Reddy, V. Gopikrishna, and M. Huber. "Inverse Reinforcement Learning for Decentralized Non-Cooperative Multiagent Systems", In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'12), Seoul, South Korea, 2012.
R. Fakoor and M. Huber. "Improving Tractability of POMDPs by Separation of Decision and Perceptual Processes", In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'12), Seoul, South Korea, 2012.
M. Oladell and M. Huber. "Symbol Generation and Feature Selection for Reinforcement Learning Agents Using Affordances and U-Trees", In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'12), Seoul, South Korea, 2012.
R. Fakoor and M. Huber M. "A Sampling-Based Approach to Reducing the Complexity of Continuous State Space POMDPs by Decomposition into Coupled Perceptual and Decision Processes", In Proceedings of the International Conference on Machine Learning and Applications (ICMLA'12), Boca Raton, FL, 2012.
T.S. Reddy, G.V. Zaruba, and M. Huber. "Game Theoretic Framework for Communication in Fully Observable Multiagent Systems", In Proceedings of the International Conference on Machine Learning and Applications (ICMLA'12), Boca Raton, FL, 2012.
T.M. Dockins and M. Huber. "Social Influence Modeling for Utility Functions in Model Predictive Control", In Proceedings of the 25th International FLAIRS Conference (FLAIRS'12), Marco Island, FL, 2012.
M. Oladell and M. Huber. "Symbol Generation and Grounding for Reinforcement Learning Agents Using Affordances and Dictionary Compression", In Proceedings of the 25th International FLAIRS Conference (FLAIRS'12), Marco Island, FL, 2012.
P-H. Ciu and M. Huber. "Reinforcement Field". In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'11) (Anchorage, AK, 2011).
S. Rajendran and M. Huber. "Autonomous Identification, Categorization and Generalization of Policies Based on Task Type". In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'11), Anchorage, AK, 2011.
P-H. Ciu and M. Huber. "Generalized Reinforcement Learning with Concept-Driven Abstract Actions". In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'11), Anchorage, AK, 2011.
S. Thirumuruganathan and M. Huber. "Building Bayesian Network based Expert Systems from Rules". In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'11), Anchorage, AK, 2011.
J. B. Brown and M. Huber. "Pseudo-Hierarchical Ant-Based Clustering: Using Automatic Boundary Formation and a Heterogeneous Agent Hierarchy to Improve Ant-Based Clustering Performance," in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'10) (Istanbul, Turkey, 2010).
J. B. Brown and M. Huber. "Pseudo-Hierarchical Ant-Based Clustering Using a Heterogeneous Agent Hierarchy and Automatic Boundary Formation," in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'10) (Portland, OR, 2010).
C. F. Jr, D. Nguyen, G. B. Guerra, and H. M. Filho. "Identification of Static and Dynamic Muscle Activation Patterns for Intuitive Human/Computer Interfaces," in Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA'10) (Samos, Greece, 2010).
A. Iqbal, L. Zhou, M. Huber, and G. Zaruba. "Optimizing Path of Mobile Beacon with Genetic Algorithm to Localize Sensor Network," in Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA'10) (Samos, Greece, 2010).
B. Holbert and M. Huber. "Design and Evaluation of Haptic Effects for Use in a Computer Desktop for the Physically Disabled," International Journal on Universal Access in the Information Society, 2010.
V. Gopikrishna and M. Huber. "A Temporal Potential Function Approach For Path Planning in Dynamic Environments," in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'09) (San Antonio, TX, 2009).
G. D'Silva and M. Huber. "Encoding User Motion Preferences in Harmonic Function Path Planning," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'09) (Saint Louis, MO, 2009).
S. Rajendran and M. Huber. "Generalizing and Categorizing Skills in Reinforcement Learning Agents Using Partial Policy Homomorphisms," in Proceedings of the 22nd International FLAIRS Conference (Sanibel Island, FL, 2009).
K. Hsiao, P. Nangeroni, M. Huber, A. Saxena, and A. Y. Ng. "Reactive Grasping Using Optical Proximity Sensors," in Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA'09) (Kobe, Japan, 2009).
P. Chiu and M. Huber. "Clustering Similar Actions in Sequential Decision Processes," in Proceedings of the 8th International Conference on Machine Learning and Applications (ICMLA'09) (Miami Beach, FL, 2009).
J. H. C. Staton and M. Huber. "An Assistive Navigation Paradigm Using Force Feedback," in Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO'09) (Tokyo, Japan, 2009).
S. Rajendran and M. Huber. "Learning to Generalize and Reuse Skills Using Approximate Partial Policy Homomorphisms," in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'09) (San Antonio, TX, 2009).
Ramachandran, D., Maiti, A., Mwangi, G., Huber, M., Levine, D., Kamangar, F., Schoech, R., & Zaruba, G. (2008). Technology to Help Patients Adhere to Treatment Plans - A Brief Introduction to the Teleherence Project. Poster session presented at Biotechnology and Bioinformatics Symposium (BIOT'08), Arlington, TX.
Reddy, T. S., Levine, D., Huber, M., & Ranganathan, N. (2008). BGrid - Component based Architecture for Bioinformatics on the Grid. Poster session presented at Biotechnology and Bioinformatics Symposium (BIOT'08), Arlington, TX.
P. Djurdjevic and M. Huber. "Learning Task Decomposition and Exploration Shaping for Reinforcement Learning Agents," in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC'08) (Singapore, 2008).
A. Loganathan and M. Huber. "An Approach for Behavior Discovery Using Clustering of Dynamics," in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC'08) (Singapore, 2008).
M. Asadi and M. Huber. "Automatic Formation of Abstract State Space Representations for Reinforcement Learning Agents," in Proceedings of the International Conference on Security and Management (SAM 2008) (Las Vegas, NV, 2008).
B. Holbert and M. Huber. "Design and Evaluation of Haptic Effects for the Use in a Desktop for the Physically Disabled," in Proceedings of the International Conference on Pervasive Technologies Related to Assistive Environments (PETRA'08) (Athens, Greece, 2008).
B. Holbert and M. Huber. "Building a Haptically Enhanced Computer Desktop for the Physically Disabled Using a Force Feedback Mouse," in Proceedings of the IASTED International Conference on Assistive Technology (AT'08) (Baltimore, MD, 2008).
H. Ryu and M. Huber. "A Particle Filter Approach for Multi-Target Tracking," in Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems (IROS'07) (San Diego, CA, 2007).
R. Huang, G. V. Zaruba, and M. Huber. "Complexity and Error Propagation of Localization Using Interferometric Ranging," in IEEE International Conference on Communications (ICC'07) (Glasgow, Scotland, 2007).
"Learning Query Reformulations for Personalized Web Search Using a Probabilistic Inference Network," in AAAI 2007 Workshop on Intelligent Techniques for Web Personalization (Vancouver, BC, Canada, 2007).
"Effective Control Knowledge Transfer Through Learning Skill and Representation Hierarchies," in Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI'07) (Hyderabad, India, 2007).
G. V. Zaruba, M. Huber, F. Kamangar, and I. Chlamtac. "Location Tracking Using RSSI Reading from a Single Access Point," ACM/Kluwer Journal of Wireless Networks, vol. 13, no. 2, pp. 221-235, 2007.
E. Torres-Verdin and M. Huber. "Learning Personalized Query Modifications," in Proceedings of the 19th International FLAIRS Conference (Melbourne Beach, FL, 2006).
A. Sabbi and M. Huber. "Particle Filter Based Object Tracking in a Stereo Vision System," in Proceedings of the IEEE International Conference on Robotics and Automation (Orlando, FL, 2006).
M. Asadi, V. N. Papudesi, and M. Huber. "Learning Skill and Representation Hierarchies for Effective Control Knowledge Transfer," in ICML 2005 Workshop on Structural Knowledge Transfer for Machine Learning (Pittsburgh, PA, 2006).
V. N. Papudesi and M. Huber. "Learning Behaviorally Grounded State Representations for Reinforcement Learning Agents," in Proceedings of the Sixth International Conference on Epigenetic Robotics (Paris, France, 2006).
Asadi, M. & Huber, M. (2005). Learning State and Action Hierarchies for Reinforcement Learning Using Autonomous Subgoal Discovery and Action-Dependent State Space Partitioning. CSE@UTA Technical Report CSE-2005-12.
G. Zaruba, F. Kamangar, M. Huber, and D. Levine. "Connect - A Personal Remote Messaging and Monitoring System to Aid People with Disabilities," IEEE Communications, vol. 43, no. 9, pp. 101-109, 2005.
Asadi, M. & Huber, M. (2005). Accelerating Action Dependent Hierarchical Reinforcement Learning Through Autonomous Subgoal Discovery. Paper presented at ICML 2005 Workshop on Rich Representations for Reinforcement Learning, Bonn, Germany.
Asadi, M. & Huber, M. (2005). Hierarchical State Abstraction with Subgoal Discovery Using Learned Policies. Paper presented at International Conference on Machine Learning; Models, Technologies and Applications, Las Vegas, NV.
Rajendran, S. & Huber, M. (2005). Learning Task-Specific Sensing, Control and Memory Policies. Paper presented at International Journal on Artificial Intelligence Tools,.
Elliott, F. & Huber, M. (2005). Learning Macros with an Enhanced LZ78 Algorithm. Paper presented at Prodeedings of the 18th International FLAIRS Conference, Clearwater Beach, FL.
Grupen, R. A. & Huber, M. (2005). A Framework for the Development of Robot Behavior. Paper presented at AAAI Spring Symposium on Developmental Robotics, Stanford, CA.
Seshadri, V., Zaruba, G. V., & Huber, M. (2005). A Bayesian Sampling Approach to In-door Localization of Wireless Devices Using Received Signal Strength Indication. Paper presented at Prodeedings of the 3rd IEEE International Conference on Pervasive Computing and Communications,.
Hannon, C. J., Burnell, L. J., & Huber, M. (2005). Research to Classroom: Experiences from a Multi-Institutional Course in Smart Home Technologies. Paper presented at SIGCSE Technical Symposium on Computer Science Education, St. Louis, MI.
Asadi, M. & Huber, M. (2005). Action Dependent State Space Abstraction for Hierarchical Learning Systems. Paper presented at Prodeedings of the IASTED International Conference on Artificial Intelligence and Applications, Insbruck, Austria.
Huber M (2000), A Hybrid Architecture for Adaptive Robot Control, PhD thesis, University of Massachusetts at Amherst.
Huber M, Grupen RA (1999), A Hybrid Architecture for Learning Robot Control Tasks, AAAI 1999 Spring Symposium : Hybrid Systems and AI - Modeling, Analysis and Control of Discrete + Continuous Systems. Stanford University, CA.
Coelho, Jr. JA, Araujo EG, Huber M, Grupen RA (1998), Contextual Control Policy Selection, Workshop on Robot Exploration and Learning/Conald'98, Pittsburgh, PA.
Huber M, Grupen RA (1996), A Hybrid Discrete Event Dynamic Systems Approach to Robot Control, UMass Computer Science technical report #96-43, October 1996.
Grupen RA, Coelho, Jr. JA, Connolly CI, Gullapalli V, Huber M, Souccar K (1994), Toward Physical Interaction and Manipulation: Screwing in a Light Bulb, AAAI 1994 Spring Symposium on Physical Interaction and Manipulation.
Grupen RA, Huber M (1993), 2D Contact Detection and Localization Using Proprioceptive Information, Proceedings of the 1993 IEEE Conference on Robotics and Automation, Vol.2, pp. 130-135, May 2-7, 1993.
Huber M, Grupen RA (1993), Contact Information from Proprioception, Proceedings of the Conference on Intelligent Autonomous Systems (IAS3), IOS Press, pp. 643-652, Feb. 1993.
Huber M, Grupen RA (1992), 2-D Contact Detection and Localization Using Proprioceptive Information, UMass Computer Science technical report #92-59, August 1992.
Introduction to the technologies needed to create an UVS (Unmanned Vehicle System). Integration of these technologies (embodied as a set of sensors, actuators, computing and mobility platform sub-systems) into a functioning UVS through team work. UVS could be designed to compete in a student competition sponsored by various technical organizations or to support a specific mission or function defined by the instructors. Prerequisite: B or better in the Introduction to Unmanned Vehicle Systems course and admission to the UVS certificate program.
Machine learning techniques that allow computers to form representations, make predictions, or apply controls automatically from data have become increasingly prevalent in modern technologies and are opening up new approaches in a wide range of domains. This course provides an introduction to the field of Machine Learning and covers fundamental and state-of-the-art machine learning algorithms. It will cover unsupervised, supervised, semi-supervised, as well as reinforcement learning techniques with a focus on unsupervised and supervised learning. Students completing this course will gain an understanding of the area of machine learning and the ways in which different learning algorithms operate. They will also be able to apply the covered methods to real-world problems.
This course is an introduction to Robotics from a computer science perspective and aimed at establishing the basis for the design and programming of autonomous robot systems. It covers basic kinematics, dynamics, and control as well as motion planning, sensors, and artificial intelligence techniques for robot applications. Emphasis is given to the application of these techniques to simulated and real robots. Throughout the course students will work individually and in groups to analyze robot control problems and to design hardware and software solutions. Students successfully completing this course will be able to write basic control programs for different robot platforms and to apply state-of-the-art artificial intelligence techniques to the control of robotic mechanisms.
Introduction to UVS (Unmanned Vehicle Systems) such as UAS (Unmanned Aircraft Systems), UGS (Unmanned Ground System) and UMS (Unmanned Maritime System), their history, missions, capabilities, types, configurations, subsystems, and the disciplines needed for UVS development and operation. UVS missions could include student competitions sponsored by various technical organizations. This course is team-taught by engineering faculty. Prerequisite: Admission to a professional engineering or science program.
Multiagent systems has emerged as an important research area with applications in many fields of computer science, including artificial intelligence, e-commerce, sensor networks, distributed computing and information retrieval, information security, and robotics. In multiagent systems, multiple autonomous entities with their own objectives have to interact and make decisions. This course explores techniques for the modeling, design, decision making, and communication in these systems. While the course will focus on frameworks, methodologies, and algorithms, it will investigate (and illustrate) them in the context of a wide range of application areas, including multi-robot systems, distributed scheduling and resource allocation, sensor networks, distributed information extraction, and network security.
With Computer Science moving into applications in the real world and involving large quantities of data, uncertainty and random variations become increasingly important aspects to be considered when designing algorithms, addressing large scale problems, modeling processes, or evaluating data. To do this, probabilistic methods for data analysis and modeling become essential tools within every branch of Computer Science.
Students successfully completing this course will have gained a solid understanding of probabilistic data modeling, interpretation, and analysis an thus have formed an important basis for more advanced courses in Computer Science as well as for the handling and analysis of data used in real-life applications and research.
This course explores modern reasoning techniques for the extraction of information from noisy data sources, for the integration of multiple information streams, and for decision-making in the presence of uncertainty. While this course will investigate these techniques often in the context of physical sensor applications and robotics, they are also applicable in a wide range of other fields including mobile networking, data mining, and control of physical processes. Students completing this course will gain an understanding of advanced methods to work with uncertain data and be able to apply them to real world problems.
This course is the second part of the two semester capstone class. The purpose of this class is to provide a "close to real world" experience in developing real products, the right way. Students in this course will learn a lot about the development process and discover some interesting things about themselves as a member of a development team along the way! This is the CSE capstone course, where many of the things learned in previous courses are put together before students tackle the real world. The course will study the product development environment used in the computer industry, and practice a phased system/software development process, often called the modified-Waterfall system development life cycle, as applied to computer hardware and software design projects. Throughout this course sequence, students will work on teams of 4-5 students. In this first course in the sequence (CSE 4316) students will identify their team and their project and start the planning process. Within this second semester, students will continue and complete, through demonstration of a working prototype, the project started in the previous semester in the same team.
Analysis and design of an industry-type project that involves hardware and software components to meet desired needs within realistic constraints and standards. The project is to be completed in CSE 4317 the following semester. Multidisciplinary teams of CSE 4316 students are required to develop, review, and present problem definition, project planning, requirements formulation, and design specification. Corequisite: CSE 4314 Prerequisites: CSE 3310, CSE 3320, and for CpE Majors CSE 3442.
Machine learning techniques are increasingly employed in a wide range of areas to model and analyze data as well as to facilitate decision support and autonomous decision making by computer systems. Reinforcement learning is an important machine learning paradigm in particular in the context of decision support and decision making, but also in the context of modeling when only limited feedback is available. This course will introduce the Reinforcement Learning paradigm and its underlying formalisms, and will cover a wide range of basic and advanced Reinforcement Learning algorithms as well as aspects of model learning, hierarchy and abstraction, and reward modeling. Throughout, this course will study these techniques in the context of a wide range of application areas, including robotics, computer vision, security, control, scheduling, and data analysis. Students completing this course will gain an understanding of the field and be able to apply modern, state-of-the-art Reinforcement Learning techniques to a wide range of problems and applications.
This course gives an introduction to the philosophies and techniques of Artificial Intelligence. AI techniques have become an essential element in modern computer software and are thus essential for a successful career and advanced studies in computer science. Students successfully completing this course will be able to apply a variety of techniques for the design of efficient algorithms for complex problems. Topics covered in this course include search algorithms (such as breadth-first, depth-first, A*), game-playing algorithms (such as Minimax), knowledge and logic reasoning, planning methods (such as STRIPS and Partially Ordered Planner), probabilistic reasoning, and machine learning.
This course introduces and applies the AI techniques necessary for an agent to act intelligently in the ``real'' world. Techniques include uncertainty reasoning, learning, natural language processing, vision and speech processing. Basic AI techniques will be reviewed in the context of the Java programming language which will be used for implementing the more advanced techniques. Emphasis will be on implementation and experimentation with the goal of building robust intelligent agents.

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