A Sandbox In Which To Learn And Develop Soar Agents,
2017
Air Force Institute of Technology
A Sandbox In Which To Learn And Develop Soar Agents, Daniel Lugo
Theses and Dissertations
It is common for military personnel to leverage simulations (and simulators) as cost-effective tools to train and become proficient at various tasks (e.g., flying an aircraft and/or performing a mission, among others). These training simulations often need to represent humans within the simulated world in a realistic manner. Realistic implies creating simulated humans that exhibit behaviors that mimic real-world decision making and actions. Typically, to create the decision-making logic, techniques developed from the domain of artificial intelligence are used. Although there are several approaches to developing intelligent agents; we focus on leveraging and open source project called Soar, to define …
Vision-Based Mobile Robotic Platform For Autonomous Landing Of Quadcopters,
2017
University of Nebraska at Omaha
Vision-Based Mobile Robotic Platform For Autonomous Landing Of Quadcopters, Timothy R. Joe
UNO Student Research and Creative Activity Fair
This project deals with the development of a vision-based control algorithm to assist quadcopters in the landing process. For demonstration purposes, the approach has been implemented in a mobile robotic platform (turtlebot). In this project, the objective is to use the mobile robot as a landing platform. The camera on-board the mobile robot detects the quadcopter (AprilTag attached to the flying robot) and keeps track of it. Based on this idea, the proposed approach estimates in real-time the landing zone. Once this zone is calculated, the mobile robot moves towards this area, stops under the quadcopter, and acts as a …
Passive Chemical Detection System For Uavs,
2017
University of Nebraska at Omaha
Passive Chemical Detection System For Uavs, John Hare 2185222
UNO Student Research and Creative Activity Fair
In this project we address the problem of autonomously detecting airborne gas particles using gas sensors that are mobilized using unmanned aerial vehicles (UAVs). The main hypothesis we investigate is whether a commercially available, off-the-shelf gas sensor can be suitably integrated on a UAV platform to detect ambient gas particles. The main challenges in this problem include addressing the weight constraints of the UAV’s payload and registering a consistent reading on the gas sensor in the presence of the turbulence in the air caused by the UAV’s rotors. To verify our hypothesis, we designed a passive funneling mechanism for airborne …
A Modular Robotic System For Assessment And Exercise Of Human Movement,
2017
University of Nebraska at Omaha
A Modular Robotic System For Assessment And Exercise Of Human Movement, Mohan Sai Ambati
UNO Student Research and Creative Activity Fair
This project targets the problem of developing a wearable modular robotic system, for assessing human movement and providing different types of exercises for the user. The system attempts to provide not only a variety of exercises (concentric, eccentric, assisted and resisted), but also to assess the change in variability of the movement as the subject shows functional improvement. The system will not only be useful for patients with sensorimotor problem such as stroke, Parkinson’s, cerebral palsy, but also for special populations such as astronauts who spend long periods of time in space and experience muscle atrophy. In this work, a …
Privacy In Context-Aware Mobile Crowdsourcing Systems,
2017
Singapore Management University
Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning …
Feature Learning Via Partial Differential Equation With Applications To Face Recognition,
2017
Singapore Management University
Feature Learning Via Partial Differential Equation With Applications To Face Recognition, Cong Fang, Zhenyu Zhao, Pan Zhou, Zhouchen Lin
Research Collection School Of Computing and Information Systems
Feature learning is a critical step in pattern recognition, such as image classification. However, most of the existing methods cannot extract features that are discriminative and at the same time invariant under some transforms. This limits the classification performance, especially in the case of small training sets. To address this issue, in this paper we propose a novel Partial Differential Equation (PDE) based method for feature learning. The feature learned by our PDE is discriminative, also translationally and rotationally invariant, and robust to illumination variation. To our best knowledge, this is the first work that applies PDE to feature learning …
The Wonders Of The Spreadsheet Tool For Data Management And Insights,
2017
Singapore Management University
The Wonders Of The Spreadsheet Tool For Data Management And Insights, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Ask any student at the Singapore Management University (SMU) toname one of the most practical and useful courses offered by theuniversity. The answer would inevitably include CAT. CAT stands forthe "Computer as an Analysis Tool" course. Originally based on acourse of the same title offered by the Wharton Business School, thefocus of CAT was shifted to provide business students the essentialpractical skills and necessary “real-world” exposure to better usepersonal computers for resolving business problems. The course isbasically centred on using the Excel spreadsheet to work onambiguous ill-defined problems (Leong & Cheong, 2009). Over theyears, three editions of a textbook have …
Directed Acyclic Graph Continuous Max-Flow Image Segmentation For Unconstrained Label Orderings,
2017
Robarts Research Institute
Directed Acyclic Graph Continuous Max-Flow Image Segmentation For Unconstrained Label Orderings, John Sh Baxter, Martin Rajchl, A. Jonathan Mcleod, Jing Yuan, Terry M. Peters
Robarts Imaging Publications
Label ordering, the specification of subset–superset relationships for segmentation labels, has been of increasing interest in image segmentation as they allow for complex regions to be represented as a collection of simple parts. Recent advances in continuous max-flow segmentation have widely expanded the possible label orderings from binary background/foreground problems to extendable frameworks in which the label ordering can be specified. This article presents Directed Acyclic Graph Max-Flow image segmentation which is flexible enough to incorporate any label ordering without constraints. This framework uses augmented Lagrangian multipliers and primal–dual optimization to develop a highly parallelized solver implemented using GPGPU. This …
Exploring Algorithms To Recognize Similar Board States In Arimaa,
2017
Rowan University
Exploring Algorithms To Recognize Similar Board States In Arimaa, Malik Khaleeque Ahmed
Theses and Dissertations
The game of Arimaa was invented as a challenge to the field of game-playing artificial intelligence, which had grown somewhat haughty after IBM's supercomputer Deep Blue trounced world champion Kasparov at chess. Although Arimaa is simple enough for a child to learn and can be played with an ordinary chess set, existing game-playing algorithms and techniques have had a difficult time rising up to the challenge of defeating the world's best human Arimaa players, mainly due to the game's impressive branching factor. This thesis introduces and analyzes new algorithms and techniques that attempt to recognize similar board states based on …
Toward A Collaborative Ai Framework For Assistive Dementia Care,
2017
Singapore Management University
Toward A Collaborative Ai Framework For Assistive Dementia Care, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We envision an integrated framework for supporting the development and deployment of human-aware, general artificial intelligence (AI) that needs to collaborate in uncertain, changing environments. We examine the technology and system requirements of building assistive care agents for dementia or cognitive impaired patients through the continuum of care. We summarize the new AI capabilities and show examples of how an evolving, adaptive development approach would be able to support the basic functionalities and applications in a sound, practical, and scalable manner. We highlight the challenges and the opportunities involved in realizing the proposed framework, and call for future research and …
An Efficient Approach To Model-Based Hierarchical Reinforcement Learning,
2017
National University of Singapore
An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.
A Riemannian Network For Spd Matrix Learning,
2017
Singapore Management University
A Riemannian Network For Spd Matrix Learning, Zhiwu Huang, Gool L. Van
Research Collection School Of Computing and Information Systems
Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model. In particular, we devise bilinear mapping layers to transform input SPD matrices to more desirable SPD matrices, exploit eigenvalue rectification layers to apply a non-linear activation function to the new SPD matrices, and design an eigenvalue logarithm layer to perform Riemannian computing …
Optimizing Expectation With Guarantees In Pomdps,
2017
Singapore Management University
Optimizing Expectation With Guarantees In Pomdps, Krishnendu Chatterjee, Guillermo A. Perez, Jean-François Raskin, Dorde Zikelic
Research Collection School Of Computing and Information Systems
A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem …
Collective Multiagent Sequential Decision Making Under Uncertainty,
2017
Singapore Management University
Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Multiagent sequential decision making has seen rapid progress with formal models such as decentralized MDPs and POMDPs. However, scalability to large multiagent systems and applicability to real world problems remain limited. To address these challenges, we study multiagent planning problems where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our work exploits recent advances in graphical models for modeling and inference with a population of individuals such as collective graphical models and the notion of finite partial exchangeability in lifted inference. We develop a collective decentralized MDP model where policies can be computed based …
Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction,
2017
Singapore Management University
Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for …
Decentralized Planning In Stochastic Environments With Submodular Rewards,
2017
Singapore Management University
Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar
Research Collection School Of Computing and Information Systems
Decentralized Markov Decision Process (Dec-MDP) providesa rich framework to represent cooperative decentralizedand stochastic planning problems under transition uncertainty.However, solving a Dec-MDP to generate coordinatedyet decentralized policies is NEXP-Hard. Researchershave made significant progress in providing approximate approachesto improve scalability with respect to number ofagents. However, there has been little or no research devotedto finding guarantees on solution quality for approximateapproaches considering multiple (more than 2 agents)agents. We have a similar situation with respect to the competitivedecentralized planning problem and the StochasticGame (SG) model. To address this, we identify models in thecooperative and competitive case that rely on submodular rewards,where we show …
Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems,
2017
Singapore Management University
Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing …
Malware Detection Using The Index Of Coincidence,
2017
San Jose State University
Malware Detection Using The Index Of Coincidence, Bhavna Gurnani
Master's Projects
In this research, we apply the Index of Coincidence (IC) to problems in malware analysis. The IC, which is often used in cryptanalysis of classic ciphers, is a technique for measuring the repeat rate in a string of symbols. A score based on the IC is applied to a variety of challenging malware families. We nd that this relatively simple IC score performs surprisingly well, with superior results in comparison to various machine learning based scores, at least in some cases.
Imitating The Brain: Autonomous Robots Harnessing The Power Of Artificial Neural Networks,
2017
Connecticut College
Imitating The Brain: Autonomous Robots Harnessing The Power Of Artificial Neural Networks, Mohammad Khan
Computer Science Honors Papers
Artificial Neural Networks (ANNs) imitate biological neural networks, which can have billions of neurons with trillions of interconnections. The first half of this paper focuses on fully-connected ANNs and hardware neural networks. The latter half of this paper focuses on Deep Learning, a strategy in Artificial Intelligence based on massive ANN architectures. We focus on Deep Convolutional Neural Networks, some of which are capable of differentiating between thousands of objects by self-learning from millions of images. We complete research in two areas of focus within the field of ANNs, and we provide ongoing work for and recommend two more areas …
Performance Verification For Robot Missions In Uncertain Environments,
2017
Fordham University
Performance Verification For Robot Missions In Uncertain Environments, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang
Faculty Publications
Abstract—Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results …
