Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches

Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Dynamic Bayesian Networks as a Probabilistic Metamodel for Combat Simulations

Simulation modeling is used in many situations. Simulation meta-modeling is used to estimate a simulation model result by representing the space of simulation model responses. Metamodeling methods are particularly useful when the simulation model is not particularly suited to real-time or mean real-time use. Most metamodeling methods provide expected value responses while some situations need probabilistic responses. This research establishes the viability of Dynamic Bayesian Networks for simulation metamodeling, those situations needing probabilistic responses. A bootstrapping method is introduced to reduce simulation data requirement for a DBN, and experimental design is shown to benefit a DBN used to represent a multi-dimensional response space. An improved interpolation method is developed and shown beneficial to DBN metamodeling applications. These contributions are employed in a military modeling case study to fully demonstrate the viability of DBN metamodeling for Defense analysis application

Using and Interpreting the Bayesian Optimization Algorithm to Improve Early Stage Design of Marine Structures.

Early stage naval structural design continues to advance as designers seek to improve the quality and speed of the design process. The early stages of design produce preliminary dimensions or scantlings which control the cost and structural performance of a vessel. Increased complexity in the evaluation of structural response has led to a need for efficient algorithms well suited to solving structural design specific optimization problems. As problem sizes increase, existing optimizers can become slow or inaccurate. The Bayesian Optimization Algorithm (BOA) is presented as one solution to efficiently solve problems in the structural design optimization process. The Bayesian optimization algorithm is an Estimation of Distribution Algorithm (EDA) that uses a statistical sample of potential design solutions to create and train a Bayesian network (BN). The application of BNs is well suited for nearly decomposable problem composition which closely matches rules based structural design evaluation. This makes the BOA well suited to solve complex early stage structural optimization problems. Additionally, the learning processes used to create and train the BNs can be analyzed and interpreted to capture design knowledge. This return of knowledge to the designer helps to improve designer intuition and model synthesis in the face of more complex and intricate models. The BNs are thus analyzed to augment design problem understanding and explore trade-offs within the design space. The result matches a paradigm shift in early stage optimization of naval structures. Designers gain better understanding of critical design variables and their interactions as compared to the previous focus on the single most optimal solution. This leads to efficient simulations which rapidly explore design spaces, document critical design variable relationships and enable the designer to create better early stage design solutions.PhDNaval Architecture and Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133317/1/tedevine_1.pd

Fast Decision-making under Time and Resource Constraints

Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, a fielder is required to intercept balls traveling in ballistic trajectories, while the motion of the fielder is constrained to the ground plane. In order to maximize the probability of interception, the agent must make good, yet timely, decisions. Researchers have put forth several heuristic approaches to describe how a fielder may decide how to run based only on immediately available information under different control paradigms. This research statistically quantifies upper bounds on the expected catch rate of a couple notable approaches, given that interception of the ball is theoretically possible if the fielder ran directly towards the landing spot with maximal effort throughout the entire duration of the ball’s flight. Additionally, novel modifications are made to a belief-space variant of iterative Linear Quadratic Gaussian (iLQG), which is an online method that may be used to find locally-optimal policies to continuous Partially Observable Markov Decision Processes (POMDPs) in which Bayesian estimation may reasonably be approximated by an Extended Kalman Filter (EKF). Directional derivatives are used to reduce the computation time of certain matrix derivatives with respect to the variance of the belief state from to , where is the dimension of the belief space. However, the improved algorithm still may not be capable of real-time decision-making by the standards of modern-day computing on mobile platforms, especially in systems with long planning horizons and sparse rewards. The belief-space variant of iLQG is applied to the outfielder problem, which may also indicate its applicability to similar target interception problems with input constraints, such as missile defense

Debating Space Security: Capabilities and Vulnerabilities

The U.S. position in the debate on space security has been that (1) space-based systems could be developed and used to obtain decisive warghting superiority over an adversary, and (2) these space-based systems, because they might give such an inordinate advantage over any adversary, will be attacked. The Russians and Chinese, in contrast, claim to be threatened by U.S. aspirations in space but deny that they pose a serious threat to U.S. space-based systems. They view the development of advanced military space systems by the United States as evidence of a growing gap of military capabilities limited only by technological--not political--constraints. They argue that U.S. missile defense systems operating in coordination with advanced satellite sensors would weaken their nuclear retaliatory potential. This dissertation argues that the positions held by both of these parties are more extreme than warranted. An analytical evaluation quickly narrows the touted capabilities and assumed vulnerabilities of space systems to a much smaller set of concerns that can be addressed by collaboration. Chapter 2: Operationally Responsive Space (ORS): Is 24/7 Warghter Support Feasible? demonstrates the infeasibility of dramatically increasing U.S. warfighting superiority by using satellites. Chapter 3: What Can be Achieved by Attacking Satellites? makes the case that although U.S. armed forces rely extensively on its satellite infrastructure, that does not immediately make them desirable targets. The functions performed by military satellites are diffused among large constellations with redundancies. Also, some of the functions performed by these satellites can be substituted for by other terrestrial and aerial systems. Chapter 4: The Limits of Chinese Anti-Satellite Missiles demonstrates that anti-satellite (ASAT) intercepts are very complex under realistic conditions and that a potential adversary with space capabilities comparable to China's has very limited capability to use ASATs in a real-world battle scenario. Finally, in order to evaluate the chief concern raised by the Russians and Chinese, chapter 5: Satellites, Missile Defense and Space Security simulates a boost-phase missile defense system cued by the advanced Space Tracking and Surveillance (STSS) sensors. It demonstrates that even under best case assumptions, the STSS sensors are not good enough for the boost-phase missile defense system to successfully intercept and destroy an ICBM. Together, these chapters aim to narrow the contentions in the debate on space security thereby fostering the international colloboration and data sharing needed to ensure safe operations in space