Engineering Commons^™

The Interacting Multiple Models Algorithm With State-Dependent Value Assignment, Rastin Rastgoufard

LSU New Orleans Theses and Dissertations

The value of a state is a measure of its worth, so that, for example, waypoints have high value and regions inside of obstacles have very small value. We propose two methods of incorporating world information as state-dependent modifications to the interacting multiple models (IMM) algorithm, and then we use a game's player-controlled trajectories as ground truths to compare the normal IMM algorithm to versions with our proposed modifications. The two methods involve modifying the model probabilities in the update step and modifying the transition probability matrix in the mixing step based on the assigned values of different target states. …

Message Passing Algorithms For Map Estimation Using Dc Programming, Akshat Kumar, Shlomo Zilberstein, Marc Toussaint

Research Collection School Of Computing and Information Systems

We address the problem of finding the most likely assignment or MAP estimation in a Markov random field. We analyze the linear programming formulation of MAP through the lens of difference of convex functions (DC) programming, and use the concave-convex procedure (CCCP) to develop efficient message-passing solvers. The resulting algorithms are guaranteed to converge to a global optimum of the well-studied local polytope, an outer bound on the MAP marginal polytope. To tighten the outer bound, we show how to combine it with the mean-field based inner bound and, again, solve it using CCCP. We also identify a useful relationship …

Provable De-Anonymization Of Large Datasets With Sparse Dimensions, Anupam Datta, Divya Sharma, Arunesh Sinha

Research Collection School Of Computing and Information Systems

There is a significant body of empirical work on statistical de-anonymization attacks against databases containing micro-dataabout individuals, e.g., their preferences, movie ratings, or transactiondata. Our goal is to analytically explain why such attacks work. Specifically, we analyze a variant of the Narayanan-Shmatikov algorithm thatwas used to effectively de-anonymize the Netflix database of movie ratings. We prove theorems characterizing mathematical properties of thedatabase and the auxiliary information available to the adversary thatenable two classes of privacy attacks. In the first attack, the adversarysuccessfully identifies the individual about whom she possesses auxiliaryinformation (an isolation attack). In the second attack, the adversarylearns additional …

Trust And Reputation For Successful Software Self-Organisation, Pierpaolo Dondio, Jean Marc Seigneur

Books/Book Chapters

Abstract An increasing number of dynamic software evolution approaches is com- monly based on integrating or utilising new pieces of software. This requires reso- lution of issues such as ensuring awareness of newly available software pieces and selection of most appropriate software pieces to use. Other chapters in this book dis- cuss dynamic software evolution focusing primarily on awareness, integration and utilisation of new software pieces, paying less attention on how selection among different software pieces is made. The selection issue is quite important since in the increasingly dynamic software world quite a few new software pieces occur over time, …

Online Path Planning And Control Solution For A Coordinated Attack Of Multiple Unmanned Aerial Vehicles In A Dynamic Environment, Juan Vega-Nevarez

Electronic Theses and Dissertations

The role of the unmanned aerial vehicle (UAV) has significantly expanded in the military sector during the last decades mainly due to their cost effectiveness and their ability to eliminate the human life risk. Current UAV technology supports a variety of missions and extensive research and development is being performed to further expand its capabilities. One particular field of interest is the area of the low cost expendable UAV since its small price tag makes it an attractive solution for target suppression. A swarm of these low cost UAVs can be utilized as guided munitions or kamikaze UAVs to attack …

The Application Of Fuzzy Granular Computing For The Analysis Of Human Dynamic Behavior In 3d Space, Murad Mohammad Alaqtash

Open Access Theses & Dissertations

Human dynamic behavior in space is very complex in that it involves many physical, perceptual and motor aspects. It is tied together at a sensory level by linkages between vestibular, visual and somatosensory information that develop through experience of inertial and gravitational reaction forces. Coordinated movement emerges from the interplay among descending output from the central nervous system, sensory input from the body and environment, muscle dynamics, and the emergent dynamics of the whole neuromusculoskeletal system.

There have been many attempts to directly capture the activities of the neuronal system in human locomotion without the ability to clarify how the …

Scale Invariant Object Recognition Using Cortical Computational Models And A Robotic Platform, Danny Voils

Dissertations and Theses

This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX …

Memristor-Based Reservoir Computing, Manjari S. Kulkarni

Dissertations and Theses

In today's nanoscale era, scaling down to even smaller feature sizes poses a significant challenge in the device fabrication, the circuit, and the system design and integration. On the other hand, nanoscale technology has also led to novel materials and devices with unique properties. The memristor is one such emergent nanoscale device that exhibits non-linear current-voltage characteristics and has an inherent memory property, i.e., its current state depends on the past. Both the non-linear and the memory property of memristors have the potential to enable solving spatial and temporal pattern recognition tasks in radically different ways from traditional binary transistor-based …

Object Retrieval From Secure Unknown Interior Spaces Using Autonomous Unmanned Aerial Vehicles, John Levous, Julie Hoven, Victor Habgood, Abdulrahman Alotaibi, Brandon Ordway, Garibe Mohammed-Jones, Haole Guo, Filip Cuckov, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

This paper describes an autonomous unmanned aerial vehicle (UAV) designed to participate in the 23rd annual International Aerial Robotics Competition. The UAV is equipped with onboard sensors and a Harvard architecture 8-bit RISC microcontroller to monitor and locally control its flight telemetry. Additional sensors (and an additional microcontroller) are used for detecting and mapping of structural and environmental objects while the UAV is in flight. The microcontrollers are interfaced with wireless communication modules for transmitting flight telemetry and structural/environmental data to a ground control station that sends the UAV command and control signals required for the mission objectives. The UAV …

Model Individualization For Real-Time Operator Functional State Assessment, Guangfan Zhang, Roger Xu, Wei Wang, Aaron A. Pepe, Feng Li, Jiang Li, Frederick Mckenzie, Tom Schnell, Nick Anderson, Dean Heitkamp

Electrical & Computer Engineering Faculty Publications

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement …

A Sentiment Analysis Of Singapore Presidential Election 2011 Using Twitter Data With Census Correction, Murphy Junyu Choy, Michelle Lee Fong Cheong, Nang Laik Ma, Ping Shung Koo

Research Collection School Of Computing and Information Systems

Sentiment analysis is a new area in text analytics where it focuses on the analysis and understanding of the human emotions from the text patterns. This new form of analysis has been widely adopted in customer relationship management especially in the context of complaint management. However, sentiment analysis using Twitter data has remained extremely difficult to manage due to sampling biasness. In this paper, we will discuss about the application of reweighting techniques in conjunction with online sentiment divisions to predict the vote percentage that individual presidential candidate in Singapore will receive in the Presidential Election 2011. There will be …

Preface: Trends In Natural And Machine Intelligence, Jonathan H. Chan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Trends in natural and machine intelligence are increasingly reflecting a convergence in these two well-established fields of study. The Third International Neural Network Society Winter Conference (INNS-WC 2012) was held in Bangkok, Thailand, on October 3-5, 2012. INNS-WC2012, with an aim to bring together scientists, practitioners, and students worldwide, to discuss the past, present, and future challenges and trends in the area of natural and machine intelligence. This event has been a bi-annual conference of the International Neural Network Society (INNS) to provide a forum for international researchers to exchange latest ideas and advances on neural networks and related discipline.

Robust Local Search For Solving Rcpsp/Max With Durational Uncertainty, Na Fu, Hoong Chuin Lau, Pradeep Varakantham, Fei Xiao

Research Collection School Of Computing and Information Systems

Scheduling problems in manufacturing, logistics and project management have frequently been modeled using the framework of Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the importance of these problems, providing scalable solution schedules for RCPSP/max problems is a topic of extensive research. However, all existing methods for solving RCPSP/max assume that durations of activities are known with certainty, an assumption that does not hold in real world scheduling problems where unexpected external events such as manpower availability, weather changes, etc. lead to delays or advances in completion of activities. Thus, in this paper, our …

Robust Distributed Scheduling Via Time Period Aggregation, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments on a real-life resource allocation problem from a container port, we show that, under stochastic conditions, the performance variation …

A Pomdp Model For Guiding Taxi Cruising In A Congested Urban City, Lucas Agussurja, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a partially observable Markov decision process (POMDP) model for improving a taxi agent cruising decision in a congested urban city. Using real-world data provided by a large taxi company in Singapore as a guide, we derive the state transition function of the POMDP. Specifically, we model the cruising behavior of the drivers as continuous-time Markov chains. We then apply dynamic programming algorithm for finding the optimal policy of the driver agent. Using a simulation, we show that this policy is significantly better than a greedy policy in congested road network.

Allocating Resources In Multiagent Flowshops With Adaptive Auctions, Hoong Chuin Lau, Zhengyi Zhao, Sam Shuzhi Ge, Thong Heng Lee

Research Collection School Of Computing and Information Systems

In this paper, we consider the problem of allocating machine resources among multiple agents, each of which is responsible to solve a flowshop scheduling problem. We present an iterated combinatorial auction mechanism in which bid generation is performed within each agent, while a price adjustment procedure is performed by a centralized auctioneer. While this approach is fairly well-studied in the literature, our primary innovation is in an adaptive price adjustment procedure, utilizing variable step-size inspired by adaptive PID-control theory coupled with utility pricing inspired by classical microeconomics. We compare with the conventional price adjustment scheme proposed in Fisher (1985), and …

Taxisim: A Multiagent Simulation Platform For Evaluating Taxi Fleet Operations, Shih-Fen Cheng, Thi Duong Nguyen

Research Collection School Of Computing and Information Systems

Taxi service is an important mode of public transportation in most metropolitan areas since it provides door-to-door convenience in the public domain. Unfortunately, despite all the convenience taxis bring, taxi fleets are also extremely inefficient to the point that over 50% of its operation time could be spent in idling state. Improving taxi fleet operation is an extremely challenging problem, not just because of its scale, but also due to fact that taxi drivers are self-interested agents that cannot be controlled centrally. To facilitate the study of such complex and decentralized system, we propose to construct a multiagent simulation platform …

Artificial Intelligence – I: A Preliminary Framework For Human-Agent Communication In Electronic Negotiations, Moez Ur Rehman, Nosheen Riaz

International Conference on Information and Communication Technologies

Electronic negotiations are business negotiations conducted via electronic means using information and communications technologies (ICT). Two dominant types of electronic negotiation systems are automated negotiation systems for software agents and negotiation support systems (NSSs) for humans. However, the integration of two types for human-agent negotiations is an important task. In this paper, an extended communication model for human-agent business negotiations is presented. For this purpose, the underlying communication models of automated negotiations and NSSs are analyzed. The extended communication model is based on a common negotiation ontology which captures the negotiation agenda and paves the way for such hybrid communication, …

Finding Robust-Under-Risk Solutions For Flowshop Scheduling, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose and explore, in the context of benchmark problems for flowshop scheduling, a risk-based concept of robustness for optimization problems. This risk-based concept is in distinction to, and complements, the uncertainty-based concept employed in the field known as robust optimization. Implementation of our concept requires problem solution methods that sample the solution space intelligently and that produce large numbers of distinct sample points. With these solutions to hand, their robustness scores are easily obtained and heuristically robust solutions found. We find evolutionary computation to be effective for this purpose on these problems.

Message-Passing Algorithms For Quadratic Programming Formulations Of Map Estimation, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Computing maximum a posteriori (MAP) estimation in graphical models is an important inference problem with many applications. We present message-passing algorithms for quadratic programming (QP) formulations of MAP estimation for pairwise Markov random fields. In particular, we use the concave-convex procedure (CCCP) to obtain a locally optimal algorithm for the non-convex QP formulation. A similar technique is used to derive a globally convergent algorithm for the convex QP relaxation of MAP. We also show that a recently developed expectation-maximization (EM) algorithm for the QP formulation of MAP can be derived from the CCCP perspective. Experiments on synthetic and real-world problems …

Scalable Multiagent Planning Using Probabilistic Inference, Akshat Kumar, Shlomo Zilberstein, Marc Toussaint

Research Collection School Of Computing and Information Systems

Multiagent planning has seen much progress with the development of formal models such as Dec-POMDPs. However, the complexity of these models -- NEXP-Complete even for two agents -- has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical model in which likelihood maximization is equivalent to plan optimization. Using the Expectation-Maximization framework for likelihood maximization, we show that the necessary inference can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We …

Real-World Parameter Tuning Using Factorial Design With Parameter Decomposition, Aldy Gunawan, Hoong Chuin Lau, Elaine Wong

Research Collection School Of Computing and Information Systems

In this paper, we explore the idea of improving the efficiency of factorial design for parameter tuning of metaheuristics. In a standard full factorial design, the number of runs increases exponentially as the number of parameters. To reduce the parameter search space, one option is to first partition parameters into disjoint categories. While this may be done manually based on user guidance, an automated approach proposed in this paper is to apply a fractional factorial design to partition parameters based on their main effects where each partition is then tuned independently. With a careful choice of fractional design, our approach …

Distributed Model Shaping For Scaling To Decentralized Pomdps With Hundreds Of Agents, Prasanna Velagapudi, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri

Research Collection School Of Computing and Information Systems

The use of distributed POMDPs for cooperative teams has been severely limited by the incredibly large joint policy- space that results from combining the policy-spaces of the individual agents. However, much of the computational cost of exploring the entire joint policy space can be avoided by observing that in many domains important interactions between agents occur in a relatively small set of scenarios, previously defined as coordination locales (CLs) [11]. Moreover, even when numerous interactions might occur, given a set of individual policies there are relatively few actual interactions. Exploiting this observation and building on an existing model shaping algorithm, …

Incremental Dcop Search Algorithms For Solving Dynamic Dcop Problems, William Yeoh, Pradeep Varakantham, Xiaoxun Sun, Sven Koenig

Research Collection School Of Computing and Information Systems

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper …

Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato

Research Collection School Of Computing and Information Systems

In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …

Decentralized Decision Support For An Agent Population In Dynamic And Uncertain Domains, Pradeep Reddy Varakantham, Shih-Fen Cheng, Thi Duong Nguyen

Research Collection School Of Computing and Information Systems

This research is motivated by problems in urban transportation and labor mobility, where the agent flow is dynamic, non-deterministic and on a large scale. In such domains, even though the individual agents do not have an identity of their own and do not explicitly impact other agents, they have implicit interactions with other agents. While there has been much research in handling such implicit effects, it has primarily assumed controlled movements of agents in static environments. We address the issue of decision support for individual agents having involuntary movements in dynamic environments . For instance, in a taxi fleet serving …

Message-Passing Algorithms For Large Structured Decentralized Pomdps, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDPs provide a rigorous framework for multi-agent decision-theoretic planning. However, their high complexity has limited scalability. In this work, we present a promising new class of algorithms based on probabilistic inference for infinite-horizon ND-POMDPs---a restricted Dec-POMDP model. We first transform the policy optimization problem to that of likelihood maximization in a mixture of dynamic Bayes nets (DBNs). We then develop the Expectation-Maximization (EM) algorithm for maximizing the likelihood in this representation. The EM algorithm for ND-POMDPs lends itself naturally to a simple message-passing paradigm guided by the agent interaction graph. It is thus highly scalable w.r.t. the number of …

Random Keys Genetic Algorithms Scheduling And Rescheduling Systems For Common Production Systems, Elkin Rodriguez-Velasquez

Engineering Management & Systems Engineering Theses & Dissertations

The majority of scheduling research deals with problems in specific production environments with specific objective functions. However, in many cases, more than one problem type and/or objective function exists, resulting in the need for a more generic and flexible system to generate schedules. Furthermore, most of the published scheduling research focuses on creating an optimal or near optimal initial schedule during the planning phase. However, after production processes start, circumstances like machine breakdowns, urgent jobs, and other unplanned events may render the schedule suboptimal, obsolete or even infeasible resulting in a "rescheduling" problem, which is typically also addressed for a …

Holistic Network Defense: Fusing Host And Network Features For Attack Classification, Jenny W. Ji

Theses and Dissertations

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon …

Kernelized Locality-Sensitive Hashing For Fast Image Landmark Association, Mark A. Weems

Theses and Dissertations

As the concept of war has evolved, navigation in urban environments where GPS may be degraded is increasingly becoming more important. Two existing solutions are vision-aided navigation and vision-based Simultaneous Localization and Mapping (SLAM). The problem, however, is that vision-based navigation techniques can require excessive amounts of memory and increased computational complexity resulting in a decrease in speed. This research focuses on techniques to improve such issues by speeding up and optimizing the data association process in vision-based SLAM. Specifically, this work studies the current methods that algorithms use to associate a current robot pose to that of one previously …