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- Genre: Academic theses
Description
Modern, advanced statistical tools from data mining and machine learning have become commonplace in molecular biology in large part because of the “big data” demands of various kinds of “-omics” (e.g., genomics, transcriptomics, metabolomics, etc.). However, in other fields of biology where empirical data sets are conventionally smaller, more traditional statistical methods of inference are still very effective and widely used. Nevertheless, with the decrease in cost of high-performance computing, these fields are starting to employ simulation models to generate insights into questions that have been elusive in the laboratory and field. Although these computational models allow for exquisite control over large numbers of parameters, they also generate data at a qualitatively different scale than most experts in these fields are accustomed to. Thus, more sophisticated methods from big-data statistics have an opportunity to better facilitate the often-forgotten area of bioinformatics that might be called “in-silicomics”.
As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
ContributorsSeto, Christian (Author) / Pavlic, Theodore (Thesis advisor) / Li, Jing (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2018
Description
A Graph Neural Network (GNN) is a type of neural network architecture that operates on data consisting of objects and their relationships, which are represented by a graph. Within the graph, nodes represent objects and edges represent associations between those objects. The representation of relationships and correlations between data is unique to graph structures. GNNs exploit this feature of graphs by augmenting both forms of data, individual and relational, and have been designed to allow for communication and sharing of data within each neural network layer. These benefits allow each node to have an enriched perspective, or a better understanding, of its neighbouring nodes and its connections to those nodes. The ability of GNNs to efficiently process high-dimensional node data and multi-faceted relationships among nodes gives them advantages over neural network architectures such as Convolutional Neural Networks (CNNs) that do not implicitly handle relational data. These quintessential characteristics of GNN models make them suitable for solving problems in which the correspondences among input data are needed to produce an accurate and precise representation of these data. GNN frameworks may significantly improve existing communication and control techniques for multi-agent tasks by implicitly representing not only information associated with the individual agents, such as agent position, velocity, and camera data, but also their relationships with one another, such as distances between the agents and their ability to communicate with one another. One such task is a multi-agent navigation problem in which the agents must coordinate with one another in a decentralized manner, using proximity sensors only, to navigate safely to their intended goal positions in the environment without collisions or deadlocks. The contribution of this thesis is the design of an end-to-end decentralized control scheme for multi-agent navigation that utilizes GNNs to prevent inter-agent collisions and deadlocks. The contributions consist of the development, simulation and evaluation of the performance of an advantage actor-critic (A2C) reinforcement learning algorithm that employs actor and critic networks for training that simultaneously approximate the policy function and value function, respectively. These networks are implemented using GNN frameworks for navigation by groups of 3, 5, 10 and 15 agents in simulated two-dimensional environments. It is observed that in $40\%$ to $50\%$ of the simulation trials, between 70$\%$ to 80$\%$ of the agents reach their goal positions without colliding with other agents or becoming trapped in deadlocks. The model is also compared to a random run simulation, where actions are chosen randomly for the agents and observe that the model performs notably well for smaller groups of agents.
ContributorsAyalasomayajula, Manaswini (Author) / Berman, Spring (Thesis advisor) / Mian, Sami (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical
methods for coping with sparse rewards during reinforcement learning modify the
reward landscape so as to better guide the learner. In contrast, this work combines
RL with a planner in order to utilize other information about the environment. As
the scope for representing environmental information is limited in RL, this work has
conflated a model-free learning algorithm – temporal difference (TD) learning – with
a Hierarchical Task Network (HTN) planner to accommodate rich environmental
information in the algorithm. In the perpetual sparse rewards problem, rewards
reemerge after being collected within a fixed interval of time, culminating in a lack of a
well-defined goal state as an exit condition to the problem. Incorporating planning in
the learning algorithm not only improves the quality of the solution, but the algorithm
also avoids the ambiguity of incorporating a goal of maximizing profit while using
only a planning algorithm to solve this problem. Upon occasionally using the HTN
planner, this algorithm provides the necessary tweak toward the optimal solution. In
this work, I have demonstrated an on-policy algorithm that has improved the quality
of the solution over vanilla reinforcement learning. The objective of this work has
been to observe the capacity of the synthesized algorithm in finding optimal policies to
maximize rewards, awareness of the environment, and the awareness of the presence
of other agents in the vicinity.
ContributorsNandan, Swastik (Author) / Pavlic, Theodore (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
Aggregation is a fundamental principle of animal behavior; it is especially significant tohighly social species, like ants. Ants typically aggregate their workers and brood in a central nest, potentially due to advantages in colony defense and regulation of the environment. In many ant species, when a colony must abandon its nest, it can effectively reach consensus on a new home. Ants of the genus Temnothorax have become a model for this collective decision-making process, and for decentralized cognition more broadly. Previous studies examine emigration by well-aggregated colonies, but can these ants also reach consensus when the colony has been scattered? Such scattering may readily occur in nature if the nest is disturbed by natural or man- made disasters. In this exploratory study, Temnothorax rugatulus colonies were randomly scattered in an arena and presented with a binary equal choice of nest sites. Findings concluded that the colonies were able to re-coalesce, however consensus is more difficult than for aggregated colonies and involved an additional primary phase of multiple temporary aggregations eventually yielding to reunification. The maximum percent of colony utilization for these aggregates was reached within the first hour, after which point, consensus tended to rise as aggregation decreased. Small, but frequent, aggregates formed within the first twenty minutes and remained and dissolved to the nest by varying processes. Each colony included a clump containing the queen, with the majority of aggregates containing at least one brood item. These findings provide additional insight to house-hunting experiments in more naturally
challenging circumstances, as well as aggregation within Temnothorax colonies.
ContributorsGoodland, Brooke (Author) / Shaffer, Zachary (Thesis advisor) / Pratt, Stephen (Thesis advisor) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2023
Description
The concept of multi-scale, heterogeneous modeling is well-known to be central in the complexities of natural and built systems. Therefore, whole models that have parts with different spatiotemporal scales are preferred to those specified using a monolithic modeling approach and tightly integrated. To build simulation frameworks that are expressive and flexible, model composability is crucial where a whole model's structure and behavior traits must be concisely specified according to those of its parts and their interactions. To undertake the spatiotemporal model composability, a breast cancer cells chemotaxis exemplar is used. In breast cancer biology, the receptors CXCR4+ and CXCR7+ and the secreting CXCL12+ cells are implicated in spreading normal and malignant cells. As discrete entities, these can be modeled using Agent-Based Modeling (ABM). The receptors and ligand bindings with chemokine diffusion regulate the cells' movement gradient. These continuous processes can be modeled as Ordinary Differential Equations (ODE) and Partial Differential Equations (PDE). A customized, text-based BrSimulator exists to model and simulate this kind of breast cancer phenomenon. To build a multi-scale, spatiotemporal simulation framework supporting model composability, this research proposes using composable cellular automata (CCA) modeling. Toward this goal, the Cellular Automata DEVS (CA-DEVS) model is used, and the novel Composable Cellular Automata DEVS (CCA-DEVS) modeling is proposed. The DEVS-Suite simulator is extended to support CA and CCA Parallel DEVS models. This simulator introduces new capabilities for controlled and modular run-time animation and superdense time trajectory visualization. Furthermore, this research proposes using the Knowledge Interchange Broker (KIB) approach to model and simulate the interactions between separate geo-referenced CCA models developed using the DEVS and Modelica modeling languages. To demonstrate the proposed model composability approach and its use in the extended DEVS-Suite simulator, the breast cancer cells chemotaxis and others have been studied. The BrSimulator is used as a proxy for evaluating the proposed model composability approach using an integrated DEVS-Suite and OpenModelica simulator. Simulation experiments are developed that show the composition of spatiotemporal ABM, ODE, and PDE models reproduce the behaviors of the same model developed in the BrSimulator.
ContributorsZhang, Chao (Author) / Sarjoughian, Hessam S (Thesis advisor) / Crook, Sharon (Committee member) / Collofello, James (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2021
Description
Component-based models are commonly employed to simulate discrete dynamicalsystems. These models lend themselves to formalizing the structures of systems at multiple levels of granularity. Visual development of component-based models serves to simplify the iterative and incremental model specification activities. The Parallel Discrete Events System Specification (DEVS) formalism offers a flexible yet rigorous approach for decomposing a whole model into its components or alternatively, composing a whole model from components. While different concepts, frameworks, and tools offer a variety of visual modeling capabilities, most pose limitations, such as visualizing multiple model hierarchies at any level with arbitrary depths. The visual and persistent layout of any number of hierarchy levels of models can be maintained and navigated seamlessly. Persistence storage is another capability needed for the modeling, simulating, verifying, and validating lifecycle. These are important features to improve the demanding task of creating and changing modular, hierarchical simulation models. This thesis proposes a new approach and develops a tool for the visual development of models. This tool supports storing and reconstructing graphical models using a NoSQL database. It offers unique capabilities important for developing increasingly larger and more complex models essential for analyzing, designing, and building Digital Twins.
ContributorsMohite, Sheetal Chandrakant (Author) / Sarjoughian, Hessam S (Thesis advisor) / Bryan, Chris (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2023
Description
In recent years, the development of Control Barrier Functions (CBF) has allowed safety guarantees to be placed on nonlinear control affine systems. While powerful as a mathematical tool, CBF implementations on systems with high relative degree constraints can become too computationally intensive for real-time control. Such deployments typically rely on the analysis of a system's symbolic equations of motion, leading to large, platform-specific control programs that do not generalize well. To address this, a more generalized framework is needed. This thesis provides a formulation for second-order CBFs for rigid open kinematic chains. An algorithm for numerically computing the safe control input of a CBF is then introduced based on this formulation. It is shown that this algorithm can be used on a broad category of systems, with specific examples shown for convoy platooning, drone obstacle avoidance, and robotic arms with large degrees of freedom. These examples show up to three-times performance improvements in computation time as well as 2-3 orders of magnitude in the reduction in program size.
ContributorsPietz, Daniel Johannes (Author) / Fainekos, Georgios (Thesis advisor) / Vrudhula, Sarma (Thesis advisor) / Pedrielli, Giulia (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
Social insects collectively exploit food sources by recruiting nestmates, creating positive feedback that steers foraging effort to the best locations. The nature of this positive feedback varies among species, with implications for collective foraging. The mass recruitment trails of many ants are nonlinear, meaning that small increases in recruitment effort yield disproportionately large increases in recruitment success. The waggle dance of honeybees, in contrast, is believed to be linear, meaning that success increases proportionately to effort. However, the implications of this presumed linearityhave never been tested. One such implication is the prediction that linear recruiters will equally exploit two identical food sources, in contrast to nonlinear recruiters, who randomly choose only one of them. I tested this prediction in colonies of honeybees that were isolated in flight cages and presented with two identical sucrose feeders. The results from 15 trials were consistent with linearity, with many cases of equal exploitation of the feeders. In addition, I tested the prediction that linear recruiters can reallocate their forager distribution when unequal feeders are swapped in position. Results from 15 trials were consistent with linearity, with many cases of clear choice for a stronger food source, followed by a subsequent switch with reallocation of foragers to the new location of the stronger food source. These findings show evidence of a linear pattern of nestmate recruitment, with implications for how colonies effectively distribute their foragers across
available resources.
ContributorsAlam, Showmik (Author) / Shaffer, Zachary (Thesis advisor) / Pratt, Stephen C (Thesis advisor) / Ozturk, Cahit (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020
Description
In certain ant species, groups of ants work together to transport food and materials back to their nests. In some cases, the group exhibits a leader-follower behavior in which a single ant guides the entire group based on its knowledge of the destination. In some cases, the leader role is occupied temporarily by an ant, only to be replaced when an ant with new information arrives. This kind of behavior can be very useful in uncertain environments where robot teams work together to transport a heavy or bulky payload. The purpose of this research was to study ways to implement this behavior on robot teams.
In this work, I combined existing dynamical models of collective transport in ants to create a stochastic model that describes these behaviors and can be used to control multi-robot systems to perform collective transport. In this model, each agent transitions stochastically between roles based on the force that it senses the other agents are applying to the load. The agent’s motion is governed by a proportional controller that updates its applied force based on the load velocity. I developed agent-based simulations of this model in NetLogo and explored leader-follower scenarios in which agents receive information about the transport destination by a newly informed agent (leader) joining the team. From these simulations, I derived the mean allocations of agents between “puller” and “lifter” roles and the mean forces applied by the agents throughout the motion.
From the simulation results obtained, we show that the mean ratio of lifter to puller populations is approximately 1:1. We also show that agents using the role update procedure based on forces are required to exert less force than agents that select their role based on their position on the load, although both strategies achieve similar transport speeds.
In this work, I combined existing dynamical models of collective transport in ants to create a stochastic model that describes these behaviors and can be used to control multi-robot systems to perform collective transport. In this model, each agent transitions stochastically between roles based on the force that it senses the other agents are applying to the load. The agent’s motion is governed by a proportional controller that updates its applied force based on the load velocity. I developed agent-based simulations of this model in NetLogo and explored leader-follower scenarios in which agents receive information about the transport destination by a newly informed agent (leader) joining the team. From these simulations, I derived the mean allocations of agents between “puller” and “lifter” roles and the mean forces applied by the agents throughout the motion.
From the simulation results obtained, we show that the mean ratio of lifter to puller populations is approximately 1:1. We also show that agents using the role update procedure based on forces are required to exert less force than agents that select their role based on their position on the load, although both strategies achieve similar transport speeds.
ContributorsGah, Elikplim (Author) / Berman, Spring M (Thesis advisor, Committee member) / Pavlic, Theodore (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020