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Title: THE PROTEOMICS APPROACH TO EVOLUTIONARY COMPUTATION: AN ANALYSIS OF PROTEOME-BASED LOCATION INDEPENDENT REPRESENTATIONS BASEDON THE PROPORTIONAL GENETIC ALGORITHM.
Creator: Garibay, Ivan, Wu, Annie, University of Central Florida
Abstract / Description: As the complexity of our society and computational resources increases, so does the complexity of the problems that we approach using evolutionary search techniques. There are recent approaches to deal with the problem of scaling evolutionary methods to cope with highly complex difficult problems. Many of these approaches are biologically inspired and share an underlying principle: a problem representation based on basic representational building blocks that interact and self-organize into...
Show moreAs the complexity of our society and computational resources increases, so does the complexity of the problems that we approach using evolutionary search techniques. There are recent approaches to deal with the problem of scaling evolutionary methods to cope with highly complex difficult problems. Many of these approaches are biologically inspired and share an underlying principle: a problem representation based on basic representational building blocks that interact and self-organize into complex functions or designs. The observation from the central dogma of molecular biology that proteins are the basic building blocks of life and the recent advances in proteomics on analysis of structure, function and interaction of entire protein complements, lead us to propose a unifying framework of thought for these approaches: the proteomics approach. This thesis propose to investigate whether the self-organization of protein analogous structures at the representation level can increase the degree of complexity and ``novelty'' of solutions obtainable using evolutionary search techniques. In order to do so, we identify two fundamental aspects of this transition: (1) proteins interact in a three dimensional medium analogous to a multiset; and (2) proteins are functional structures. The first aspect is foundational for understanding of the second. This thesis analyzes the first aspect. It investigates the effects of using a genome to proteome mapping on evolutionary computation. This analysis is based on a genetic algorithm (GA) with a string to multiset mapping that we call the proportional genetic algorithm (PGA), and it focuses on the feasibility and effectiveness of this mapping. This mapping leads to a fundamental departure from typical EC methods: using a multiset of proteins as an intermediate mapping results in a \emph{completely location independent} problem representation where the location of the genes in a genome has no effect on the fitness of the solutions. Completely location independent representations, by definition, do not suffer from traditional EC hurdles associated with the location of the genes or positional effect in a genome. Such representations have the ability to self-organize into a genomic structure that appears to favor positive correlations between form and quality of represented solutions. Completely location independent representations also introduce new problems of their own such as the need for large alphabets of symbols and the theoretical need for larger representation spaces than traditional approaches. Overall, these representations perform as well or better than traditional representations and they appear to be particularly good for the class of problems involving proportions or multisets. This thesis concludes that the use of protein analogous structures as an intermediate representation in evolutionary computation is not only feasible but in some cases advantageous. In addition, it lays the groundwork for further research on proteins as functional self-organizing structures capable of building increasingly complex functionality, and as basic units of problem representation for evolutionary computation.
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Date Issued: 2004
Identifier: CFE0000311, ucf:46307
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000311

Title: Effective Task Transfer Through Indirect Encoding.
Creator: Verbancsics, Phillip, Stanley, Kenneth, Sukthankar, Gita, Georgiopoulos, Michael, Garibay, Ivan, University of Central Florida
Abstract / Description: An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from...
Show moreAn important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird's eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation.Yet a challenge for such representation is that a raw two-dimensional map is high-dimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain.
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Date Issued: 2011
Identifier: CFE0004174, ucf:49071
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004174

Title: Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions.
Creator: Pugh, Justin, Stanley, Kenneth, Wu, Annie, Sukthankar, Gita, Garibay, Ivan, University of Central Florida
Abstract / Description: Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of...
Show moreEvolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future.It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities.Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community's desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications.
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Date Issued: 2019
Identifier: CFE0007513, ucf:52638
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007513

Title: Optimization Approaches for Electricity Generation Expansion Planning Under Uncertainty.
Creator: Zhan, Yiduo, Zheng, Qipeng, Vela, Adan, Garibay, Ivan, Sun, Wei, University of Central Florida
Abstract / Description: In this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments,...
Show moreIn this dissertation, we study the long-term electricity infrastructure investment planning problems in the electrical power system. These long-term capacity expansion planning problems aim at making the most effective and efficient investment decisions on both thermal and wind power generation units. One of our research focuses are uncertainty modeling in these long-term decision-making problems in power systems, because power systems' infrastructures require a large amount of investments, and need to stay in operation for a long time and accommodate many different scenarios in the future. The uncertainties we are addressing in this dissertation mainly include demands, electricity prices, investment and maintenance costs of power generation units. To address these future uncertainties in the decision-making process, this dissertation adopts two different optimization approaches: decision-dependent stochastic programming and adaptive robust optimization. In the decision-dependent stochastic programming approach, we consider the electricity prices and generation units' investment and maintenance costs being endogenous uncertainties, and then design probability distribution functions of decision variables and input parameters based on well-established econometric theories, such as the discrete-choice theory and the economy-of-scale mechanism. In the adaptive robust optimization approach, we focus on finding the multistage adaptive robust solutions using affine policies while considering uncertain intervals of future demands.This dissertation mainly includes three research projects. The study of each project consists of two main parts, the formulation of its mathematical model and the development of solution algorithms for the model. This first problem concerns a large-scale investment problem on both thermal and wind power generation from an integrated angle without modeling all operational details. In this problem, we take a multistage decision-dependent stochastic programming approach while assuming uncertain electricity prices. We use a quasi-exact solution approach to solve this multistage stochastic nonlinear program. Numerical results show both computational efficient of the solutions approach and benefits of using our decision-dependent model over traditional stochastic programming models. The second problem concerns the long-term investment planning with detailed models of real-time operations. We also take a multistage decision-dependent stochastic programming approach to address endogenous uncertainties such as generation units' investment and maintenance costs. However, the detailed modeling of operations makes the problem a bilevel optimization problem. We then transform it to a Mathematic Program with Equilibrium Constraints (MPEC) problem. We design an efficient algorithm based on Dantzig-Wolfe decomposition to solve this multistage stochastic MPEC problem. The last problem concerns a multistage adaptive investment planning problem while considering uncertain future demand at various locations. To solve this multi-level optimization problem, we take advantage of affine policies to transform it to a single-level optimization problem. Our numerical examples show the benefits of using this multistage adaptive robust planning model over both traditional stochastic programming and single-level robust optimization approaches. Based on numerical studies in the three projects, we conclude that our approaches provide effective and efficient modeling and computational tools for advanced power systems' expansion planning.
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Date Issued: 2016
Identifier: CFE0006676, ucf:51248
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006676

Title: Value-of-Information based Data Collection in Underwater Sensor Networks.
Creator: Khan, Fahad, Turgut, Damla, Yuksel, Murat, Behal, Aman, Bassiouni, Mostafa, Garibay, Ivan, University of Central Florida
Abstract / Description: Underwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater...
Show moreUnderwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater vehicles (AUVs) to offload these large quantities of data. Such mobile sinks are called data mules. Often it is the case that a sensor network is deployed to report events that require immediate attention. Delays in reporting such events can have catastrophic consequences. In this dissertation, we present path planning algorithms that help in prioritizing data retrieval from sensor nodes in such a manner that nodes that require more immediate attention would be dealt with at the earliest. In other words, the goal is to improve the Quality of Information (QoI) retrieved. The path planning algorithms proposed in this dissertation are based on heuristics meant to improve the Value of Information (VoI) retrieved from a system. Value of information is a construct that helps in encoding the valuation of an information segment i.e. it is the price an optimal player would pay to obtain a segment of information in a game theoretic setting. Quality of information and value of information are complementary concepts. In this thesis, we formulate a value of information model for sensor networks and then consider the constraints that arise in underwater settings. On the basis of this, we develop a VoI-based path planning problem statement and propose heuristics that solve the path planning problem. We show through simulation studies that the proposed strategies improve the value, and hence, quality of the information retrieved. It is important to note that these path planning strategies can be applied equally well in terrestrial settings that deploy mobile sinks for data collection.
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Date Issued: 2019
Identifier: CFE0007476, ucf:52683
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007476

Title: The Challenges and Barriers to Employment for Female in Riyadh and Tabuk.
Creator: Almutairi, Sultan, O'Neal, Thomas, Garibay, Ivan, Keathley, Heather, Jahani, Shiva, University of Central Florida
Abstract / Description: Women labor force participation plays an important role in economic. The developing in economy in Saudi Arabia depends on men rather than women, more than 50 years the Saudi women participation in the labor force extremely is low, this dissertation seeks to identify the challenges and barriers to employment for women in Riyadh and Tabuk. This study examines three research questions. The first question explored the difference between the rate of women unemployment in Tabuk and the rate of...
Show moreWomen labor force participation plays an important role in economic. The developing in economy in Saudi Arabia depends on men rather than women, more than 50 years the Saudi women participation in the labor force extremely is low, this dissertation seeks to identify the challenges and barriers to employment for women in Riyadh and Tabuk. This study examines three research questions. The first question explored the difference between the rate of women unemployment in Tabuk and the rate of women unemployment in Riyadh. The second question investigated ways in which a logistic regression using demographics data could be used to predict the women unemployment rates in two cities. The third question investigated the challenges faced by unemployed women in two cites. An online survey was administrated to both groups. The survey included demographic information and Women Labor Force Participation Instrument. A Chi-Square test was developed from the data to test the differences of the unemployed women in two cites. In order to analyze the second question, the researcher utilized two statistical analysis tests. A logistic regression equation was developed from the data to predict unemployment rates in two cites. Additionally, Partial least squares structural equation modeling were used to analyze the exploratory research question. Content analysis was also used to analyze the challenges faced by unemployed women.
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Date Issued: 2019
Identifier: CFE0007597, ucf:52561
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007597

Title: Improvement of Data-Intensive Applications Running on Cloud Computing Clusters.
Creator: Ibrahim, Ibrahim, Bassiouni, Mostafa, Lin, Mingjie, Zhou, Qun, Ewetz, Rickard, Garibay, Ivan, University of Central Florida
Abstract / Description: MapReduce, designed by Google, is widely used as the most popular distributed programmingmodel in cloud environments. Hadoop, an open-source implementation of MapReduce, is a data management framework on large cluster of commodity machines to handle data-intensive applications. Many famous enterprises including Facebook, Twitter, and Adobehave been using Hadoop for their data-intensive processing needs. Task stragglers in MapReduce jobs dramatically impede job execution on massive datasets in...
Show moreMapReduce, designed by Google, is widely used as the most popular distributed programmingmodel in cloud environments. Hadoop, an open-source implementation of MapReduce, is a data management framework on large cluster of commodity machines to handle data-intensive applications. Many famous enterprises including Facebook, Twitter, and Adobehave been using Hadoop for their data-intensive processing needs. Task stragglers in MapReduce jobs dramatically impede job execution on massive datasets in cloud computing systems. This impedance is due to the uneven distribution of input data and computation load among cluster nodes, heterogeneous data nodes, data skew in reduce phase, resource contention situations, and network configurations. All these reasons may cause delay failure and the violation of job completion time. One of the key issues that can significantly affect the performance of cloud computing is the computation load balancing among cluster nodes. Replica placement in Hadoop distributed file system plays a significant role in data availability and the balanced utilization of clusters. In the current replica placement policy (RPP) of Hadoop distributed file system (HDFS), the replicas of data blocks cannot be evenly distributed across cluster's nodes. The current HDFS must rely on a load balancing utility for balancing the distribution of replicas, which results in extra overhead for time and resources. This dissertation addresses data load balancing problem and presents an innovative replica placement policy for HDFS. It can perfectly balance the data load among cluster's nodes. The heterogeneity of cluster nodes exacerbates the issue of computational load balancing; therefore, another replica placement algorithm has been proposed in this dissertation for heterogeneous cluster environments. The timing of identifying the straggler map task is very important for straggler mitigation in data-intensive cloud computing. To mitigate the straggler map task, Present progress and Feedback based Speculative Execution (PFSE) algorithm has been proposed in this dissertation. PFSE is a new straggler identification scheme to identify the straggler map tasks based on the feedback information received from completed tasks beside the progress of the current running task. Straggler reduce task aggravates the violation of MapReduce job completion time. Straggler reduce task is typically the result of bad data partitioning during the reduce phase. The Hash partitioner employed by Hadoop may cause intermediate data skew, which results in straggler reduce task. In this dissertation a new partitioning scheme, named Balanced Data Clusters Partitioner (BDCP), is proposed to mitigate straggler reduce tasks. BDCP is based on sampling of input data and feedback information about the current processing task. BDCP can assist in straggler mitigation during the reduce phase and minimize the job completion time in MapReduce jobs. The results of extensive experiments corroborate that the algorithms and policies proposed in this dissertation can improve the performance of data-intensive applications running on cloud platforms.
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Date Issued: 2019
Identifier: CFE0007818, ucf:52804
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007818

Title: Bootstrapping Cognitive Radio Networks.
Creator: Horine, Brent, Turgut, Damla, Wei, Lei, Boloni, Ladislau, Sukthankar, Gita, Garibay, Ivan, University of Central Florida
Abstract / Description: Cognitive radio networks promise more efficient spectrum utilization by leveraging degrees of freedom and distributing data collection. The actual realization of these promises is challenged by distributed control, and incomplete, uncertain and possibly conflicting knowledge bases. We consider two problems in bootstrapping, evolving, and managing cognitive radio networks. The first is Link Rendezvous, or how separate radio nodes initially find each other in a spectrum band with many degrees...
Show moreCognitive radio networks promise more efficient spectrum utilization by leveraging degrees of freedom and distributing data collection. The actual realization of these promises is challenged by distributed control, and incomplete, uncertain and possibly conflicting knowledge bases. We consider two problems in bootstrapping, evolving, and managing cognitive radio networks. The first is Link Rendezvous, or how separate radio nodes initially find each other in a spectrum band with many degrees of freedom, and little shared knowledge. The second is how radio nodes can negotiate for spectrum access with incomplete information.To address the first problem, we present our Frequency Parallel Blind Link Rendezvous algorithm. This approach, designed for recent generations of digital front-ends, implicitly shares vague information about spectrum occupancy early in the process, speeding the progress towards a solution. Furthermore, it operates in the frequency domain, facilitating a parallel channel rendezvous. Finally, it operates without a control channel and can rendezvous anywhere in the operating band. We present simulations and analysis on the false alarm rate for both a feature detector and a cross-correlation detector. We compare our results to the conventional frequency hopping sequence rendezvous techniques.To address the second problem, we model the network as a multi-agent system and negotiate by exchanging proposals, augmented with arguments. These arguments include information about priority status and the existence of other nodes. We show in a variety of network topologies that this process leads to solutions not otherwise apparent to individual nodes, and achieves superior network throughput, request satisfaction, and total number of connections, compared to our baselines. The agents independently formulate proposals based upon communication desires, evaluate these proposals based upon capacity constraints, create arguments in response to proposal rejections, and re-evaluate proposals based upon received arguments. We present our negotiation rules, messages, and protocol and demonstrate how they interoperate in a simulation environment.
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Date Issued: 2012
Identifier: CFE0004546, ucf:49240
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004546

Title: Design of the layout of a manufacturing facility with a closed loop conveyor with shortcuts using queueing theory and genetic algorithms.
Creator: Lasrado, Vernet, Nazzal, Dima, Mollaghasemi, Mansooreh, Reilly, Charles, Garibay, Ivan, Sivo, Stephen, Armacost, Robert, University of Central Florida
Abstract / Description: With the ongoing technology battles and price wars in today's competitive economy, every company is looking for an advantage over its peers. A particular choice of facility layout can have a significant impact on the ability of a company to maintain lower operational expenses under uncertain economic conditions. It is known that systems with less congestion have lower operational costs. Traditionally, manufacturing facility layout problem methods aim at minimizing the total distance traveled,...
Show moreWith the ongoing technology battles and price wars in today's competitive economy, every company is looking for an advantage over its peers. A particular choice of facility layout can have a significant impact on the ability of a company to maintain lower operational expenses under uncertain economic conditions. It is known that systems with less congestion have lower operational costs. Traditionally, manufacturing facility layout problem methods aim at minimizing the total distance traveled, the material handling cost, or the time in the system (based on distance traveled at a specific speed). The proposed methodology solves the looped layout design problem for a looped layout manufacturing facility with a looped conveyor material handling system with shortcuts using a system performance metric, i.e. the work in process (WIP) on the conveyor and at the input stations to the conveyor, as a factor in the minimizing function for the facility layout optimization problem which is solved heuristically using a permutation genetic algorithm. The proposed methodology also presents the case for determining the shortcut locations across the conveyor simultaneously (while determining the layout of the stations around the loop) versus the traditional method which determines the shortcuts sequentially (after the layout of the stations has been determined). The proposed methodology also presents an analytical estimate for the work in process at the input stations to the closed looped conveyor.It is contended that the proposed methodology (using the WIP as a factor in the minimizing function for the facility layout while simultaneously solving for the shortcuts) will yield a facility layout which is less congested than a facility layout generated by the traditional methods (using the total distance traveled as a factor of the minimizing function for the facility layout while sequentially solving for the shortcuts). The proposed methodology is tested on a virtual 300mm Semiconductor Wafer Fabrication Facility with a looped conveyor material handling system with shortcuts. The results show that the facility layouts generated by the proposed methodology have significantly less congestion than facility layouts generated by traditional methods. The validation of the developed analytical estimate of the work in process at the input stations reveals that the proposed methodology works extremely well for systems with Markovian Arrival Processes.
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Date Issued: 2011
Identifier: CFE0004125, ucf:49088
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004125

Title: Towards Improving Human-Robot Interaction For Social Robots.
Creator: Khan, Saad, Boloni, Ladislau, Behal, Aman, Sukthankar, Gita, Garibay, Ivan, Fiore, Stephen, University of Central Florida
Abstract / Description: Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture...
Show moreAutonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. We validated the results of the Market Patrol scenario by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. 3. We modeled the movement of a socially aware mobile robot in a dense crowds, using the concept of a micro-conflict to represent the challenge of giving or not giving way to pedestrians. 4. We developed an approach for the robot behavior in micro-conflicts based on the psychological observation that human opponents will use a consistent strategy. For this, the mobile robot classifies the opponent strategy reflected by the personality and social status of the person and chooses an appropriate counter-strategy that takes into account the urgency of the robots' mission. 5. We developed an alternative approach for the resolution of micro-conflicts based on the imitation of the behavior of the human agent. This approach aims to make the behavior of an autonomous robot closely resemble that of a remotely operated one.
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Date Issued: 2015
Identifier: CFE0005965, ucf:50819
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005965

Title: Identifying Influential Agents in Social Systems.
Creator: Maghami, Mahsa, Sukthankar, Gita, Turgut, Damla, Wu, Annie, Boloni, Ladislau, Garibay, Ivan, University of Central Florida
Abstract / Description: This dissertation addresses the problem of influence maximization in social networks. Influence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent...
Show moreThis dissertation addresses the problem of influence maximization in social networks. Influence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent us from sending the advertisement to everybody in the network. Therefore, a wise selection of the people can be beneficial in increasing the product adoption. To model these social systems, agent-based modeling, a powerful tool for the study of phenomena that are difficult to observe within the confines of the laboratory, is used.To analyze marketing scenarios, this dissertation proposes a new method for propagating information through a social system and demonstrates how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation 1) accounts for the effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results on synthetic data show that this method is significantly better than network analysis methods based on centrality measures.The optimized influence maximization (OIM) described above, has some limitations. For instance, it relies on a global estimation of the interaction among agents in the network, rendering it incapable of handling large networks. Although OIM is capable of finding the influential nodes in the social network in an optimized way and targeting them for advertising, in large networks, performing the matrix operations required to find the optimized solution is intractable.To overcome this limitation, we then propose a hierarchical influence maximization (HIM) algorithm for scaling influence maximization to larger networks. In the hierarchical method the network is partitioned into multiple smaller networks that can be solved exactly with optimization techniques, assuming a generalized IC model, to identify a candidate set of seed nodes. The candidate nodes are used to create a distance-preserving abstract version of the network that maintains an aggregate influence model between partitions. The budget limitation for the advertising dictates the algorithm's stopping point. On synthetic datasets, we show that our method comes close to the optimal node selection, at substantially lower runtime costs.We present results from applying the HIM algorithm to real-world datasets collected from social media sites with large numbers of users (Epinions, SlashDot, and WikiVote) and compare it with two benchmarks, PMIA and DegreeDiscount, to examine the scalability and performance.Our experimental results reveal that HIM scales to larger networks but is outperformed by degree-based algorithms in highly-connected networks. However, HIM performs well in modular networks where the communities are clearly separable with small number of cross-community edges. This finding suggests that for practical applications it is useful to account for network properties when selecting an influence maximization method.
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Date Issued: 2014
Identifier: CFE0005205, ucf:50647
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005205

evolutionary computation (2)	+ -
genetic algorithms (2)	+ -
AUV (1)	+ -
Advertising (1)	+ -
Agent system (1)	+ -

Argumentation (1)	+ -
BigData (1)	+ -
Cloud computing (1)	+ -
Cognitive Radio (1)	+ -
Crowd modeling (1)	+ -
Data-Intensive Computing (1)	+ -
Electricity Generation (1)	+ -
Hadoop (1)	+ -
Hadoop distributed file system (1)	+ -
Human behavior (1)	+ -
Human robot interaction (1)	+ -
Indirect Encoding (1)	+ -
Influence maximization (1)	+ -
Knowledge Representation (1)	+ -
Labor force (1)	+ -
Link Rendezvous (1)	+ -
Machine Learning (1)	+ -
Machine learning (1)	+ -
MapReduce (1)	+ -
Multi-agent system (1)	+ -
Negotiation (1)	+ -
Neural Networks (1)	+ -
Optimization (1)	+ -
Parallel and Distributed Processing (1)	+ -
QoI (1)	+ -

Garibay, Ivan (11)	+ -
University of Central Florida (11)	+ -
Sukthankar, Gita (5)	+ -
Boloni, Ladislau (3)	+ -
Turgut, Damla (3)	+ -

Wu, Annie (3)	+ -
Bassiouni, Mostafa (2)	+ -
Behal, Aman (2)	+ -
Stanley, Kenneth (2)	+ -
Almutairi, Sultan (1)	+ -
Armacost, Robert (1)	+ -
Ewetz, Rickard (1)	+ -
Fiore, Stephen (1)	+ -
Georgiopoulos, Michael (1)	+ -
Horine, Brent (1)	+ -
Ibrahim, Ibrahim (1)	+ -
Jahani, Shiva (1)	+ -
Keathley, Heather (1)	+ -
Khan, Fahad (1)	+ -
Khan, Saad (1)	+ -
Lasrado, Vernet (1)	+ -
Lin, Mingjie (1)	+ -
Maghami, Mahsa (1)	+ -
Mollaghasemi, Mansooreh (1)	+ -
Nazzal, Dima (1)	+ -
O'Neal, Thomas (1)	+ -
Pugh, Justin (1)	+ -
Reilly, Charles (1)	+ -
Sivo, Stephen (1)	+ -
Sun, Wei (1)	+ -

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