Mission-Phasing Techniques for Constrained Agents in Stochastic Environments.

Resource constraints restrict the set of actions that an agent can take, such that the agent might not be able to perform all its desired tasks. Computational time limitations restrict the number of states that an agent can model and reason over, such that the agent might not be able to formulate a policy that can respond to all possible eventualities. This work argues that, in either situation, one effective way of improving the agent's performance is to adopt a phasing strategy. Resource-constrained agents can choose to reconfigure resources and switch action sets for handling upcoming events better when moving from phase to phase; time-limited agents can choose to focus computation on high-value phases and to exploit additional computation time during the execution of earlier phases to improve solutions for future phases. This dissertation consists of two parts, corresponding to the aforementioned resource constraints and computational time limitations. The first part of the dissertation focuses on the development of automated resource-driven mission-phasing techniques for agents operating in resource-constrained environments. We designed a suite of algorithms which not only can find solutions to optimize the use of predefined phase-switching points, but can also automatically determine where to establish such points, accounting for the cost of creating them, in complex stochastic environments. By formulating the coupled problems of mission decomposition, resource configuration, and policy formulation into a single compact mathematical formulation, the presented algorithms can effectively exploit problem structure and often considerably reduce computational cost for finding exact solutions. The second part of this dissertation is the design of computation-driven mission-phasing techniques for time-critical systems. We developed a new deliberation scheduling approach, which can simultaneously solve the coupled problems of deciding both when to deliberate given its cost, and which phase decision procedures to execute during deliberation intervals. Meanwhile, we designed a heuristic search method to effectively utilize the allocated time within each phase. As illustrated in experimental results, the computation-driven mission-phasing techniques, which extend problem decomposition techniques with the across-phase deliberation scheduling and inner-phase heuristic search methods mentioned above, can help an agent generate a better policy within time limit.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60650/1/jianhuiw_1.pd

Mission Planning Techniques for Cooperative LEO Spacecraft Constellations

This research develops a mission planning approach that allows different systems to cooperate in accomplishing a single mission goal. Using the techniques described allows satellites to cooperate in efficiently maneuvering, or collecting images of Earth and transmitting the collected data to users on the ground. The individual resources onboard each satellite, like fuel, memory capacity and pointing agility, are used in a manner that ensures the goals and objectives of the mission are realized in a feasible way. A mission plan can be generated for each satellite within the cooperating group that collectively optimize the mission objectives from a global viewpoint. The unique methods and framework presented for planning the spacecraft operations are flexible and can be applied to a variety of decision making processes where prior decisions impact later decision options. This contribution to the satellite constellation mission planning field, thus has greater applicability to the wider decision problem discipline

A Lunar Surface System Supportability Technology Development Roadmap

This paper discusses the establishment of a Supportability Technology Development Roadmap as a guide for developing capabilities intended to allow NASA's Constellation program to enable a supportable, sustainable and affordable exploration of the Moon and Mars. Presented is a discussion of "supportability", in terms of space facility maintenance, repair and related logistics and a comparison of how lunar outpost supportability differs from the International Space Station. Supportability lessons learned from NASA and Department of Defense experience and their impact on a future lunar outpost is discussed. A supportability concept for future missions to the Moon and Mars that involves a transition from a highly logistics dependent to a logistically independent operation is discussed. Lunar outpost supportability capability needs are summarized and a supportability technology development strategy is established. The resulting Lunar Surface Systems Supportability Strategy defines general criteria that will be used to select technologies that will enable future flight crews to act effectively to respond to problems and exploit opportunities in a environment of extreme resource scarcity and isolation. This strategy also introduces the concept of exploiting flight hardware as a supportability resource. The technology roadmap involves development of three mutually supporting technology categories, Diagnostics Test & Verification, Maintenance & Repair, and Scavenging & Recycling. The technology roadmap establishes two distinct technology types, "Embedded" and "Process" technologies, with different implementation and thus different criteria and development approaches. The supportability technology roadmap addresses the technology readiness level, and estimated development schedule for technology groups that includes down-selection decision gates that correlate with the lunar program milestones. The resulting supportability technology roadmap is intended to develop a set of technologies with widest possible capability and utility with a minimum impact on crew time and training and remain within the time and cost constraints of the Constellation progra

A Lunar Surface System Supportability Technology Development Roadmap

This paper discusses the establishment of a Supportability Technology Development Roadmap as a guide for developing capabilities intended to allow NASA s Constellation program to enable a supportable, sustainable and affordable exploration of the Moon and Mars. Presented is a discussion of supportability, in terms of space facility maintenance, repair and related logistics and a comparison of how lunar outpost supportability differs from the International Space Station. Supportability lessons learned from NASA and Department of Defense experience and their impact on a future lunar outpost is discussed. A supportability concept for future missions to the Moon and Mars that involves a transition from a highly logistics dependent to a logistically independent operation is discussed. Lunar outpost supportability capability needs are summarized and a supportability technology development strategy is established. The resulting Lunar Surface Systems Supportability Strategy defines general criteria that will be used to select technologies that will enable future flight crews to act effectively to respond to problems and exploit opportunities in an environment of extreme resource scarcity and isolation. This strategy also introduces the concept of exploiting flight hardware as a supportability resource. The technology roadmap involves development of three mutually supporting technology categories, Diagnostics Test and Verification, Maintenance and Repair, and Scavenging and Recycling. The technology roadmap establishes two distinct technology types, "Embedded" and "Process" technologies, with different implementation and thus different criteria and development approaches. The supportability technology roadmap addresses the technology readiness level, and estimated development schedule for technology groups that includes down-selection decision gates that correlate with the lunar program milestones. The resulting supportability technology roadmap is intended to develop a set of technologies with widest possible capability and utility with a minimum impact on crew time and training and remain within the time and cost constraints of the Constellation program

Lunar Surface Systems Supportability Technology Development Roadmap

The Lunar Surface Systems Supportability Technology Development Roadmap is a guide for developing the technologies needed to enable the supportable, sustainable, and affordable exploration of the Moon and other destinations beyond Earth. Supportability is defined in terms of space maintenance, repair, and related logistics. This report considers the supportability lessons learned from NASA and the Department of Defense. Lunar Outpost supportability needs are summarized, and a supportability technology strategy is established to make the transition from high logistics dependence to logistics independence. This strategy will enable flight crews to act effectively to respond to problems and exploit opportunities in an environment of extreme resource scarcity and isolation. The supportability roadmap defines the general technology selection criteria. Technologies are organized into three categories: diagnostics, test, and verification; maintenance and repair; and scavenge and recycle. Furthermore, "embedded technologies" and "process technologies" are used to designate distinct technology types with different development cycles. The roadmap examines the current technology readiness level and lays out a four-phase incremental development schedule with selection decision gates. The supportability technology roadmap is intended to develop technologies with the widest possible capability and utility while minimizing the impact on crew time and training and remaining within the time and cost constraints of the program

Global optimisation of multiple gravity assist trajectories

Multiple gravity assist (MGA) trajectories represent a particular class of space trajectories in which a spacecraft exploits the encounter with one or more celestial bodies to change its velocity vector; they have been essential to reach high Delta-v targets with low propellant consumption. The search for optimal transfer trajectories can be formulated as a mixed combinatorial-continuous global optimisation problem; however, it is known that the problem is difficult to solve, especially if deep space manoeuvres (DSM) are considered. This thesis addresses the automatic design of MGA trajectories through global search techniques, in answer to the requirements of having a large number of mission options in a short time, during the preliminary design phase. Two different approaches are presented. The first is a two-level approach: a number of feasible planetary sequences are initially generated; then, for each one, families of the MGA trajectories are built incrementally. The whole transfer is decomposed into sub-problems of smaller dimension and complexity, and the trajectory is progressively composed by solving one problem after the other. At each incremental step, a stochastic search identifies sets of feasible solutions: this region is preserved, while the rest of the search space is pruned out. The process iterates by adding one planet-to-planet leg at a time and pruning the unfeasible portion of the solution space. Therefore, when another leg is added to the trajectory, only the feasible set for the previous leg is considered and the search space is reduced. It is shown, through comparative tests, how the proposed incremental search performs an effective pruning of the search space, providing families of optimal solutions with a lower computational cost than a non-incremental approach. Known deterministic and stochastic methods are used for the comparison. The algorithm is applied to real MGA case studies, including the ESA missions BepiColombo and Laplace. The second approach performs an integrated search for the planetary sequence and the associated trajectories. The complete design of an MGA trajectory is formulated as an autonomous planning and scheduling problem. The resulting scheduled plan provides the planetary sequence for a MGA trajectory and a good estimation of the optimality of the associated trajectories. For each departure date, a full tree of possible transfers from departure to destination is generated. An algorithm inspired by Ant Colony Optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination, adding one node at a time, using a probability function to select one of the feasible directions. Unlike standard ACO, a taboo-based heuristics prevents ants from re-exploring the same solutions. This approach is applied to the design of optimal transfers to Saturn (inspired by Cassini) and to Mercury, and it demonstrated to be very competitive against known traditional stochastic population-based techniques

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Technical ReportThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Resource Allocation Among Agents with MDP-Induced Preferences

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size