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Artificial Intelligence and Robotics Commons™

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Air Force Institute of Technology

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Full-Text Articles in Artificial Intelligence and Robotics

Data Supporting Research On Personalized Learning Paths, Sean Mochocki, Mark Reith Mar 2024

Data Supporting Research On Personalized Learning Paths, Sean Mochocki, Mark Reith

Faculty Publications

Personalized Learning Paths (PLPs) are a key application of Artificial Intelligence in E-Learning. In contrast to regular Learning Paths, they return a unique sequence of learning materials identified as meeting the individual needs of the students. In the literature, PLPs are often created from knowledge graphs, which assist with ordering topics and their associated learning materials. Knowledge graphs are typically directed and acyclic, to capture prerequisite relationships between topics, though they can also have bidirectional edges when these prerequisite relationships are not necessary. This data package provides a primarily un-directed knowledge graph, with associated repository of open-source learning materials that …

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Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox Feb 2023

Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox

Faculty Publications

Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of …

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A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz Jun 2022

A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz

Faculty Publications

The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this …

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Evolution Of Combined Arms Tactics In Heterogeneous Multi-Agent Teams, Robert J. Wilson, David W. King, Gilbert L. Peterson May 2022

Evolution Of Combined Arms Tactics In Heterogeneous Multi-Agent Teams, Robert J. Wilson, David W. King, Gilbert L. Peterson

Faculty Publications

Multi-agent systems research is concerned with the emergence of system-level behaviors from relatively simple agent interactions. Multi-agent systems research to date is primarily concerned with systems of homogeneous agents, with member agents both physically and behaviorally identical. Systems of heterogeneous agents with differing physical or behavioral characteristics may be able to accomplish tasks more efficiently than homogeneous teams, via cooperation between mutually complementary agent types. In this article, we compare the performance of homogeneous and heterogeneous teams in combined arms situations. Combined arms theory proposes that the application of heterogeneous forces, en masse, can generate effects far greater than outcomes …

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Factored Beliefs For Machine Agents In Decentralized Partially Observable Markov Decision Processes, Joshua Lapso, Gilbert L. Peterson May 2022

Factored Beliefs For Machine Agents In Decentralized Partially Observable Markov Decision Processes, Joshua Lapso, Gilbert L. Peterson

Faculty Publications

A shared mental model (SMM) is a foundational structure in high performing, task-oriented teams and aid humans in determining their teammate's goals and intentions. Higher levels of mental alignment between teammates can reduce the direct dialogue required for team success. For decision-making teams, a transactive memory system (TMS) offers team members a map of specialized knowledge, indicating source of knowledge and the source's credibility. SMM and TMS formulations aid human-agent team performance in their intended team types. However, neither improve team performance with a project team--one that requires both behavioral and knowledge integration. We present a hybrid cognitive model (HCM) …

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Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers Sep 2021

Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers

Faculty Publications

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …

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Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals May 2021

Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals

Faculty Publications

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

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A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin May 2020

A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin

Faculty Publications

In this work, the behavior of dilute interstitial helium in W–Mo binary alloys was explored through the application of a first principles-informed neural network (NN) in order to study the early stages of helium-induced damage and inform the design of next generation materials for fusion reactors. The neural network (NN) was trained using a database of 120 density functional theory (DFT) calculations on the alloy. The DFT database of computed solution energies showed a linear dependence on the composition of the first nearest neighbor metallic shell. This NN was then employed in a kinetic Monte Carlo simulation, which took into …

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Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple Feb 2020

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …

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Multiple Pursuer Multiple Evader Differential Games, Eloy Garcia, David Casbeer, Alexander Von Moll, Meir Pachter Nov 2019

Multiple Pursuer Multiple Evader Differential Games, Eloy Garcia, David Casbeer, Alexander Von Moll, Meir Pachter

Faculty Publications

In this paper an N-pursuer vs. M-evader team conflict is studied. The differential game of border defense is addressed and we focus on the game of degree in the region of the state space where the pursuers are able to win. This work extends classical differential game theory to simultaneously address weapon assignments and multi-player pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players' optimal strategies require the co-design of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is proposed and …

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Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila Nov 2018

Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila

Faculty Publications

Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that all of the relevant differences between items are captured through creating distinct items, e.g., if color matters then the same item in two different colors would have two items created, one for each color. In some domains, that is unrealistic. This paper makes two contributions. The first extends SPM algorithms to allow item differentiation through attribute variables for domains with large numbers of items, e.g, by having one item with a variable with a color attribute rather than …

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A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson Sep 2018

A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson

Faculty Publications

Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: An autonomous swarm searching for multiple dynamic targets and Conway's Game of …

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Machine Learning Nuclear Detonation Features, Daniel T. Schmitt, Gilbert L. Peterson Oct 2014

Machine Learning Nuclear Detonation Features, Daniel T. Schmitt, Gilbert L. Peterson

Faculty Publications

Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge …

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Narratives As A Fundamental Component Of Consciousness, Sandra L. Vaughan, Robert F. Mills, Michael R. Grimaila, Gilbert L. Peterson, Steven K. Rogers Jul 2014

Narratives As A Fundamental Component Of Consciousness, Sandra L. Vaughan, Robert F. Mills, Michael R. Grimaila, Gilbert L. Peterson, Steven K. Rogers

Faculty Publications

In this paper, we propose a conceptual architecture that models human (spatially-temporally-modally) cohesive narrative development using a computer representation of quale properties. Qualia are proposed to be the fundamental "cognitive" components humans use to generate cohesive narratives. The engineering approach is based on cognitively inspired technologies and incorporates the novel concept of quale representation for computation of primitive cognitive components of narrative. The ultimate objective of this research is to develop an architecture that emulates the human ability to generate cohesive narratives with incomplete or perturbated information.

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Context-Driven Image Annotation Using Imagenet, George E. Noel, Gilbert L. Peterson May 2013

Context-Driven Image Annotation Using Imagenet, George E. Noel, Gilbert L. Peterson

Faculty Publications

Image annotation research has demonstrated success on test data for focused domains. Unfortunately, extending these techniques to the broader topics found in real world data often results in poor performance. This paper proposes a novel approach that leverages WordNet and ImageNet capabilities to annotate images based on local text and image features. Signatures generated from ImageNet images based on WordNet synonymous sets are compared using Earth Mover's Distance against the query image and used to rank order surrounding words by relevancy. The results demonstrate effective image annotation, producing higher accuracy and improved specificity over the ALIPR image annotation system. Abstract …

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Quest Hierarchy For Hyperspectral Face Recognition, David M. Ryer, Trevor J. Bihl, Kenneth W. Bauer Jr., Steven K. Rogers May 2012

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 …

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Dynamic Behavior Sequencing For Hybrid Robot Architectures, Gilbert L. Peterson, Jeffrey P. Duffy, Daylond J. Hooper Nov 2011

Dynamic Behavior Sequencing For Hybrid Robot Architectures, Gilbert L. Peterson, Jeffrey P. Duffy, Daylond J. Hooper

Faculty Publications

Hybrid robot control architectures separate planning, coordination, and sensing and acting into separate processing layers to provide autonomous robots both deliberative and reactive functionality. This approach results in systems that perform well in goal-oriented and dynamic environments. Often, the interfaces and intents of each functional layer are tightly coupled and hand coded so any system change requires several changes in the other layers. This work presents the dynamic behavior hierarchy generation (DBHG) algorithm, which uses an abstract behavior representation to automatically build a behavior hierarchy for meeting a task goal. The generation of the behavior hierarchy occurs without knowledge of …

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Improving Occupancy Grid Fastslam By Integrating Navigation Sensors, Christopher Weyers, Gilbert L. Peterson Sep 2011

Improving Occupancy Grid Fastslam By Integrating Navigation Sensors, Christopher Weyers, Gilbert L. Peterson

Faculty Publications

When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use …

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Real-Time Behavior-Based Robot Control, Brian G. Wooley, Gilbert L. Peterson, Jared T. Kresge Jan 2011

Real-Time Behavior-Based Robot Control, Brian G. Wooley, Gilbert L. Peterson, Jared T. Kresge

Faculty Publications

Behavior-based systems form the basis of autonomous control for many robots, but there is a need to ensure these systems respond in a timely manner. Unexpected latency can adversely affect the quality of an autonomous system’s operations, which in turn can affect lives and property in the real-world. A robots ability to detect and handle external events is paramount to providing safe and dependable operation. This paper presents a concurrent version of a behavior-based system called the Real-Time Unified Behavior Framework, which establishes a responsive basis of behavior-based control that does not bind the system developer to any single behavior …

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Unified Behavior Framework For Reactive Robot Control, Brian G. Woolley, Gilbert L. Peterson Jul 2009

Unified Behavior Framework For Reactive Robot Control, Brian G. Woolley, Gilbert L. Peterson

Faculty Publications

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: (1) simplifies development and testing; (2) promotes the reuse of code; (3) supports designs that scale easily into large hierarchies while restricting code complexity; and (4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. …

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A Secure Group Communication Architecture For Autonomous Unmanned Aerial Vehicles, Adrian N. Phillips, Barry E. Mullins, Richard Raines, Rusty O. Baldwin Aug 2008

A Secure Group Communication Architecture For Autonomous Unmanned Aerial Vehicles, Adrian N. Phillips, Barry E. Mullins, Richard Raines, Rusty O. Baldwin

Faculty Publications

This paper investigates the application of a secure group communication architecture to a swarm of autonomous unmanned aerial vehicles (UAVs). A multicast secure group communication architecture for the low earth orbit (LEO) satellite environment is evaluated to determine if it can be effectively adapted to a swarm of UAVs and provide secure, scalable, and efficient communications. The performance of the proposed security architecture is evaluated with two other commonly used architectures using a discrete event computer simulation developed using MATLAB. Performance is evaluated in terms of the scalability and efficiency of the group key distribution and management scheme when the …

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Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson Jul 2008

Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning, Erik J. Dries, Gilbert L. Peterson

Faculty Publications

This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. This paper describes two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ant colony system algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions …

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Ant Clustering With Locally Weighting Ant Perception And Diversified Memory, Gilbert L. Peterson, Christopher B. Mayer, Thomas L. Kubler Mar 2008

Ant Clustering With Locally Weighting Ant Perception And Diversified Memory, Gilbert L. Peterson, Christopher B. Mayer, Thomas L. Kubler

Faculty Publications

Ant clustering algorithms are a robust and flexible tool for clustering data that have produced some promising results. This paper introduces two improvements that can be incorporated into any ant clustering algorithm: kernel function similarity weights and a similarity memory model replacement scheme. A kernel function weights objects within an ant’s neighborhood according to the object distance and provides an alternate interpretation of the similarity of objects in an ant’s neighborhood. Ants can hill-climb the kernel gradients as they look for a suitable place to drop a carried object. The similarity memory model equips ants with a small memory consisting …

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An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm With Application To The Detection Of Distributed Computer Network Intrusions, Charles R. Haag, Gary B. Lamont, Paul D. L. Williams, Gilbert L. Peterson Jul 2007

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm With Application To The Detection Of Distributed Computer Network Intrusions, Charles R. Haag, Gary B. Lamont, Paul D. L. Williams, Gilbert L. Peterson

Faculty Publications

Today's signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus and encoding it into a signature that is stored in its anomaly database, providing a window of vulnerability to computer systems during this time. Further, the maximum size of an Internet Protocol-based message requires the database to be huge in order to maintain possible signature combinations. In order to tighten this response cycle within storage constraints, this paper presents an innovative Artificial Immune System-inspired Multiobjective Evolutionary Algorithm. This distributed intrusion detection system (IDS) is intended to measure …

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Genetic Evolution Of Hierarchical Behavior Structures, Brian G. Woolley, Gilbert L. Peterson Jul 2007

Genetic Evolution Of Hierarchical Behavior Structures, Brian G. Woolley, Gilbert L. Peterson

Faculty Publications

The development of coherent and dynamic behaviors for mobile robots is an exceedingly complex endeavor ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behavior framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behavior design. Competitive evolution of the behavior population incrementally develops feasible solutions for the domain through competitive ranking. By developing and implementing many simple behaviors independently and then evolving …

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Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson May 2006

Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson

Faculty Publications

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function approximation presented here is that of fuzzy state aggregation. This article presents the use of fuzzy state aggregation with the current policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF), exceeding the learning rate …

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Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson Sep 2005

Cooperative Reinforcement Learning Using An Expert-Measuring Weighted Strategy With Wolf, Kevin Cousin, Gilbert L. Peterson

Faculty Publications

Gradient descent learning algorithms have proven effective in solving mixed strategy games. The policy hill climbing (PHC) variants of WoLF (Win or Learn Fast) and PDWoLF (Policy Dynamics based WoLF) have both shown rapid convergence to equilibrium solutions by increasing the accuracy of their gradient parameters over standard Q-learning. Likewise, cooperative learning techniques using weighted strategy sharing (WSS) and expertness measurements improve agent performance when multiple agents are solving a common goal. By combining these cooperative techniques with fast gradient descent learning, an agent’s performance converges to a solution at an even faster rate. This statement is verified in a …

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Cognitive Robot Mapping With Polylines And An Absolute Space Representation, Kennard R. Laviers, Gilbert L. Peterson Apr 2004

Cognitive Robot Mapping With Polylines And An Absolute Space Representation, Kennard R. Laviers, Gilbert L. Peterson

Faculty Publications

Robot mapping even today is one of the most challenging problems in robot programming. Most successful methods use some form of occupancy grid to represent a mapped region. This approach becomes problematic if the robot is mapping a large environment, the map quickly becomes too large for processing and storage. Rather than storing the map as an occupancy grid, our robot (equipped with sonars) sees the world as a series of connected spaces. These spaces are initially mapped as an occupancy grid in a room by room fashion. As the robot leaves a space, denoted by passing through a doorway, …

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