Physical Sciences and Mathematics Commons^™

Uncertain Congestion Games With Assorted Human Agent Populations, Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng

Shih-Fen CHENG

Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific …

Heuristic Algorithms For Balanced Multi-Way Number Partitioning, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Kyriakos MOURATIDIS

Balanced multi-way number partitioning (BMNP) seeks to split a collection of numbers into subsets with (roughly) the same cardinality and subset sum. The problem is NP-hard, and there are several exact and approximate algorithms for it. However, existing exact algorithms solve only the simpler, balanced two-way number partitioning variant, whereas the most effective approximate algorithm, BLDM, may produce widely varying subset sums. In this paper, we introduce the LRM algorithm that lowers the expected spread in subset sums to one third that of BLDM for uniformly distributed numbers and odd subset cardinalities. We also propose Meld, a novel strategy for …

Analog Vlsi Implementation Of Support Vector Machine Learning And Classification, Sheng-Yu Peng, Bradley Minch, Paul Hasler

Bradley Minch

We propose an analog VLSI approach to implementing the projection neural networks adapted for the supportvector machine with radial-basis kernel functions, which are realized by a proposed floating-gate bump circuit with the adjustable width. Other proposed circuits include simple current mirrors and log-domain Alters. Neither resistors nor amplifiers are employed. Therefore it is suitable for large-scale neural network implementations. We show the measurement results of the bump circuit and verify the resulting analog signal processing system on the transistor level by using a SPICE simulator. The same approach can also be applied to the support vectorregression. With these analog signal …

Forced Displacement In Colombia, Fernando Estrada

Fernando Estrada

No abstract provided.

Lagrangian Relaxation Techniques For Scalable Spatial Conservation Planning, Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land parcels within a given budget. This problem can be solved by standard integer programming methods using the sample average approximation (SAA) scheme. Our main contribution lies in exploiting the separable structure present in this problem and using Lagrangian relaxation techniques to gain scalability over the flat representation. We also generalize the approach to allow the application of the SAA scheme to a range of stochastic optimization problems. Our iterative approach is highly …

Decision Support For Agent Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Shih-Fen Cheng, Geoff Gordon, Asrar Ahmed

Research Collection School Of Computing and Information Systems

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, …

Logistics Orchestration Modeling And Evaluation For Humanitarian Relief, Hoong Chuin Lau, Zhengping Li, Xin Du, Heng Jiang, Robert De Souza

Research Collection School Of Computing and Information Systems

This paper proposes an orchestration model for post-disaster response that is aimed at automating the coordination of scarce resources that minimizes the loss of human lives. In our setting, different teams are treated as agents and their activities are "orchestrated" to optimize rescue performance. Results from simulation are analysed to evaluate the performance of the optimization model.

Incorporating Memory And Learning Mechanisms Into Meta-Raps, Arif Arin

Engineering Management & Systems Engineering Theses & Dissertations

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics.

Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning …

Decision Support For Agent Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Shih-Fen Cheng, Geoff Gordon, Asrar Ahmed

Shih-Fen CHENG

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, …

Foundations Of Inference, Kevin H. Knuth, John Skilling

Physics Faculty Scholarship

We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries. With other applications such as measure theory in mind, our derivations assume minimal symmetries, relying on neither negation nor continuity nor differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of quantifying rules that form the familiar probability calculus. We also derive a unique quantification of divergence, entropy and information.

A Location-Aware Architecture Supporting Intelligent Real-Time Mobile Applications, Sean J. Barbeau

USF Tampa Graduate Theses and Dissertations

This dissertation presents LAISYC, a modular location-aware architecture for intelligent real-time mobile applications that is fully-implementable by third party mobile app developers and supports high-precision and high-accuracy positioning systems such as GPS. LAISYC significantly improves device battery life, provides location data authenticity, ensures security of location data, and significantly reduces the amount of data transferred between the phone and server. The design, implementation, and evaluation of LAISYC using real mobile phones include the following modules: the GPS Auto-Sleep module saves battery energy when using GPS, maintaining acceptable movement tracking (approximately 89% accuracy) with an approximate average doubling of battery life. …

Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoff Gordon, Pradeep Reddy Varakantham, William Yeoh, Ajay Srinivasan, Hoong Chuin Lau, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Multi-agent planning is a well-studied problem with applications in various areas. Due to computational constraints, existing research typically focuses either on unstructured domains with many agents, where we are content with heuristic solutions, or domains with small numbers of agents or special structure, where we can find provably near-optimal solutions. In contrast, here we focus on provably near-optimal solutions in domains with many agents, by exploiting influence limits. To that end, we make two key contributions: (a) an algorithm, based on Lagrangian relaxation and randomized rounding, for solving multi-agent planning problems represented as large mixed-integer programs; (b) a proof of …

Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoffrey J. Gordon, Pradeep Varakantham, William Yeoh, Hoong Chuin Lau, Ajay Srinivasan Aravamudhan, Shih-Fen Cheng

LARC Research Publications

Multi-agent planning is a well-studied problem with applications in various areas. Due to computational constraints, existing research typically focuses either on unstructured domains with many agents, where we are content with heuristic solutions, or domains with small numbers of agents or special structure, where we can find provably near-optimal solutions. In contrast, here we focus on provably near-optimal solutions in domains with many agents, by exploiting influence limit. To that end, we make two key contributions: (a) an algorithm, based on Lagrangian relaxation and randomized rounding, for solving multi-agent planning problems represented as large mixed-integer programs; (b) a proof of …

The Advanced Educational Robot, Calder Phillips-Grafflin

Honors Theses

Existing literature in the field of computer science education clearly demonstrates that robots can be ideal teaching tools for basic computer science concepts. Likewise, robots are an ideal platform for more complicated CS techniques such as evolutionary algorithms and neural networks. With these two distinct roles in mind, that of the teaching tool and that of the research tool, in collaboration with customers in the CS department we have developed a new robotics platform suitable for both roles that provides higher performance and improved ease-of-use in comparison to the robots currently in use at Union. We have successfully designed and …

A Biologically-Inspired Affective Model Based On Cognitive Situational Appraisal, Feng Shu, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Although various emotion models have been proposed based on appraisal theories, most of them focus on designing specific appraisal rules and there is no unified framework for emotional appraisal. Moreover, few existing emotion models are biologically-inspired and are inadequate in imitating emotion process of human brain. This paper proposes a bio-inspired computational model called Cognitive Regulated Affective Architecture (CRAA), inspired by the cognitive regulated emotion theory and the network theory of emotion. This architecture is proposed by taking the following positions: (1) Cognition and emotion are not separated but interacted systems; (2) The appraisal of emotion depends on and should …

Active Malware Analysis Using Stochastic Games, Simon Williamson, Pradeep Reddy Varakantham, Debin Gao, Chen Hui Ong

Research Collection School Of Computing and Information Systems

Cyber security is increasingly important for defending computer systems from loss of privacy or unauthorised use. One important aspect is threat analysis - how does an attacker infiltrate a system and what do they want once they are inside. This paper considers the problem of Active Malware Analysis, where we learn about the human or software intruder by actively interacting with it with the goal of learning about its behaviours and intentions, whilst at the same time that intruder may be trying to avoid detection or showing those behaviours and intentions. This game-theoretic active learning is then used to obtain …

Prioritized Shaping Of Models For Solving Dec-Pomdps, Pradeep Reddy Varakantham, William Yeoh, Prasanna Velagapudi, Paul Scerri

Research Collection School Of Computing and Information Systems

An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to …

Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Subagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making, especially when perception and computational resources are limited. This paper presents a neuropsychological-inspired dual memory model for agents, consisting of an episodic memory that records the agent’s experience in real time and a semantic memory that captures factual knowledge through a parallel consolidation process. In addition, the model incorporates a natural forgetting mechanism that prevents memory overloading by removing transient memory traces. Our experimental study based on a real-time first-person-shooter video game has indicated that the memory consolidation and forgetting processes are not only …

Stochastic Dominance In Stochastic Dcops For Risk-Sensitive Applications, Nguyen Duc Thien, William Yeoh, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known probability distribution reward functions, and introduced algorithms to find solutions with the largest expected reward. Unfortunately, such a solution might be very risky, that is, very likely to result in a poor reward. Thus, in this paper, we make three contributions: (1) we propose a stricter objective for …

Delayed Observation Planning In Partially Observable Domains, Pradeep Reddy Varakantham, Janusz Marecki

Research Collection School Of Computing and Information Systems

Traditional models for planning under uncertainty such as Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) assume that the observations about the results of agent actions are instantly available to the agent. In so doing, they are no longer applicable to domains where observations are received with delays caused by temporary unavailability of information (e.g. delayed response of the market to a new product). To that end, we make the following key contributions towards solving Delayed observation POMDPs (D-POMDPs): (i) We first provide an parameterized approximate algorithm for solving D-POMDPs efficiently, with desired accuracy; and (ii) We then propose …

Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Susnagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making, especially when perception and computational resources are limited. This paper presents a neuropsychological- inspired dual memory model for agents, consisting of an episodic memory that records the agent's experience in real time and a semantic memory that captures factual knowledge through a parallel consolidation process. In addition, the model incorporates a natural forgetting mechanism that prevents memory overloading by removing transient memory traces. Our experimental study based on a real-time first-person-shooter video game has indicated that the memory consolidation and forgetting processes are not …

Multi-Tier Exploration Concept Demonstration Mission, Jeremy Straub

Jeremy Straub

A multi-tier, multi-craft mission architecture has been proposed but, despite its apparent promise, limited use and testing of the architecture has been conducted. This paper proposes and details a mission concept and its implementation for testing this architecture in the terrestrial environment. It is expected that this testing will allow significant refinement of the proposed architecture as well as providing data on its suitability for use in both terrestrial and extra-terrestrial applications. Logistical and technical challenges with this testing are discussed.

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

University of 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. …

A Location-Based Incentive Mechanism For Participatory Sensing Systems With Budget Constraints, Luis Gabriel Jaimes

USF Tampa Graduate Theses and Dissertations

Participatory Sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their

cellular phones. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would

be very difficult to assess and solve. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation and privacy concerns. Without adequate incentive and privacy guaranteeing …

Game Challenge: A Factorial Analysis Approach, Ian J. Fraser

Electronic Thesis and Dissertation Repository

Video games that customize to a player's experience level and abilities have the potential to allow a broader range of players to become engaged and maintain interest as they progress in experience level. A game that uniquely customizes the player's experience could attract additional demographics to gaming, which will result in a distinct edge in marketability and potential revenue. This thesis examines a subsection of adaptive gaming systems from the perspective of identifying game factors that alter the level of difficulty. Our focus is to provide a solution useful to both research and commercial gaming communities by developing a system …

3d Velocity Retrieval And Storm Tracking Using Multiple Radars, Yong Zhang

Electronic Thesis and Dissertation Repository

Severe weather forecasting is one of the most important and urgent tasks in the meteorology field. This thesis builds on previous work by Barron and Mercer and their graduate students, concerning the use of 3D optical flow to retrieve 3D wind velocity from 3D Doppler radial velocity datasets and tracking 3D severe weather storms using fuzzy points realized as ellipsoids to represent storms and a fuzzy algebra machinery in a relaxation labeling framework to track storms in Doppler precipitation datasets.

We first extend the original 3D optical flow (both least squares and regularization methods) for recovering 3D wind velocity from …

Quest Hierarchy For Hyperspectral Face Recognition, David M. Ryer, Trevor J. Bihl, Kenneth W. Bauer Jr., Steven K. Rogers

Faculty Publications

A qualia exploitation of sensor technology (QUEST) motivated architecture using algorithm fusion and adaptive feedback loops for face recognition for hyperspectral imagery (HSI) is presented. QUEST seeks to develop a general purpose computational intelligence system that captures the beneficial engineering aspects of qualia-based solutions. Qualia-based approaches are constructed from subjective representations and have the ability to detect, distinguish, and characterize entities in the environment Adaptive feedback loops are implemented that enhance performance by reducing candidate subjects in the gallery and by injecting additional probe images during the matching process. The architecture presented provides a framework for exploring more advanced integration …

A Spatially Explicit Agent Based Model Of Muscovy Duck Home Range Behavior, James Howard Anderson

USF Tampa Graduate Theses and Dissertations

ABSTRACT

Research in GIScience has identified agent-based simulation methodologies as effective in the study of complex adaptive spatial systems (CASS). CASS are characterized by the emergent nature of their spatial expressions and by the changing relationships between their constituent variables and how those variables act on the system's spatial expression over time. Here, emergence refers to a CASS property where small-scale, individual action results in macroscopic or system-level patterns over time. This research develops and executes a spatially-explicit agent based model of Muscovy Duck home range behavior. Muscovy duck home range behavior is regarded as a complex adaptive spatial system …

Multi-Core Unit Propagation In Functional Languages, Jonathan Alexander Leaver

Electronic Thesis and Dissertation Repository

Answer Set Programming is a declarative modeling paradigm enabling specialists in diverse disciplines to describe and solve complicated problems. Growth in high performance computing is driving ever smarter and more scalable parallel answer set solvers. To improve on today's cutting-edge, researchers need to develop increasingly intelligent methods for analysis of a solver's runtime information. Reflecting on the solver's search state typically pauses its progress until the analysis is complete. This work introduces methods from the domain of parallel functional programming and immutable type theory to construct a representation of the search state that is both amenable to introspection and efficiently …

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 …