Adaptive General Search Framework for Games and Beyond

The research field of Artificial General Intelligence (AGI) is concerned with the creation of adaptive programs that can autonomously address tasks of a different nature. Search and planning have been identified as core capabilities of AGI, and have been successful in many scenarios that require sequential decision-making. However, many search algorithms are developed for specific problems and exploit domain-specific knowledge, which makes them not applicable to perform different tasks autonomously. Although some domain-independent search algorithms have been proposed, a programmer still has to make decisions on their design, setup and enhancements. Thus, the performance is limited by the programmer's decisions, which are usually biased. This paper proposes to develop a framework that, in line with the goals of AGI, autonomously addresses a wide variety of search tasks, adapting automatically to each new, unknown task. To achieve this, we propose to encode search algorithms in a formal language and combine algorithm portfolios with automatic algorithm generation. In addition, we see games as the ideal test bed for the framework, because they can model a wide variety of complex problems. Finally, we believe that this research will have an impact not only on the AGI research field, but also on the game industry and on real-world problems

A Learning Approach To Sampling Optimization: Applications in Astrodynamics

A new, novel numerical optimization algorithm is developed, tested, and used to solve difficult numerical problems from the field of astrodynamics. First, a brief review of optimization theory is presented and common numerical optimization techniques are discussed. Then, the new method, called the Learning Approach to Sampling Optimization (LA) is presented. Simple, illustrative examples are given to further emphasize the simplicity and accuracy of the LA method. Benchmark functions in lower dimensions are studied and the LA is compared, in terms of performance, to widely used methods. Three classes of problems from astrodynamics are then solved. First, the N - impulse orbit transfer and rendezvous problems are solved by using the LA optimization technique along with derived bounds that make the problem computationally feasible. This marriage between analytical and numerical methods allows an answer to be found for an order of magnitude greater number of impulses than are currently published. Next, the N -impulse work is applied to design periodic close encounters (PCE) in space. The encounters are defined as an open rendezvous, meaning that two spacecraft must be at the same position at the same time, but their velocities are not necessarily equal. The PCE work is extended to include N -impulses and other constraints, and new examples are given. Finally, a trajectory optimization problem is solved using the LA algorithm and comparing performance with other methods based on two models-with varying complexity-of the Cassini-Huygens mission to Saturn. The results show that the LA consistently outperforms commonly used numerical optimization algorithms

Robust preliminary design for multiple gravity assist spacecraft trajectories

The development of a new spacecraft trajectory design method most often occurs because a particular capability does not exist. The invention is usually considered successful so long as it is capable of producing solutions to the problem in question, and thus satisfies a particular design requirement, or is mathematically elegant. When innovation favors the latter to the exclusion of the former, the interoperability of the new method with existent techniques, and the utility of the method in the context of the overall mission design process, from concept to fight operations, is not always realized. The concepts introduced and developed throughout this work respond to specific preliminary mission design needs, but their development is also focused on maintaining, or improving, trajectory design work flow compatibility and efficiency. The techniques described address specific contributions to the multiple gravity assist trajectory optimization state-of-the-art, however, each one is also an important component of a modern trajectory design paradigm and is valuable for its ability to be integrated with and streamline that process as a whole. The bounded-impulse approximation is a widely used method for early stage trajectory design for low and high thrust vehicles. Many previous studies involving this method of design have focused on developing new or improved trajectory transcriptions. This dissertation introduces analytic techniques for calculating the Jacobian matrix for two existing bounded-impulse trajectory models. The calculations allow for the use of a smooth spacecraft power model. One such model is introduced that handles multiple thruster on-off events and a variety of logic programs. A smooth spline-based ephemeris system is also discussed that is compatible with the analytic Jacobian formulae. Mission design activities associated with the NASA New Frontiers 4 proposal call identified a particular shortcoming of the popular Sims-Flanagan bounded-impulse model, namely that its control nodes are distributed equally in time, clustering them at apoapsis for eccentric transfers, which reduces the control authority at periapsis. In response to this, a partially regularized bounded-impulse model is introduced that distributes control nodes equally at both apses. The new transcription is capable of delivering the same mass to the target as a trajectory modeled using Sims-Flanagan, but requires far fewer control segments in the trajectory discretization to do so. A bounded-impulse trajectory is usually sufficient to generate a first order (or better) estimate of a mission's mass budget, however, these low-fidelity models can prove problematic to converge inside a flight-fidelity design tool that models fi nite-burn arcs. Low-thrust trajectories in particular suffer from this issue due to the extended period of time that the thruster operates. Analytic partial derivative computations are introduced in this work that enable the replacement of bounded-impulse maneuvers and Keplerian propagation with numerical integration arcs without reducing the runtime performance such that the model becomes unusable for preliminary design tasks. These calculations are also compatible with a smooth electric power model and accommodate final time free problems. The resulting trajectory is shown to be sufficiently accurate such that the flight heritage tool MIRAGE can track it within acceptable error limits placed on the spacecraft's state vector. Finally, improvements that this thesis makes to bounded-impulse trajectory models are leveraged to solve a challenging planetary satellite tour design problem. The robustness improvements that the analytic Jacobian formulae afford the trajectory transcription, are combined with a parallelized flyby tree path finding algorithm to produce a design framework capable of autonomously optimizing a moon tour mission similar to Galileo from launch through tour end using two-body dynamics

Multiple Gravity Assist Spacecraft Trajectories Design Based on BFS and EP_DE Algorithm

The paper deals with the multiple gravity assist trajectories design. In order to improve the performance of the heuristic algorithms, such as differential evolution algorithm, in multiple gravity assist trajectories design optimization, a method combining BFS (breadth-first search) and EP DE (differential evolution algorithm based on search space exploring and principal component analysis) is proposed. In this method, firstly find the possible multiple gravity assist planet sequences with pruning based BFS and use standard differential evolution algorithm to judge the possibility of all the possible trajectories. Then select the better ones from all the possible solutions. Finally, use EP DE which will be introduced in this paper to find an optimal decision vector of spacecraft transfer time schedule (launch window and transfer duration) for each selected planet sequence. In this paper, several cases are presented to prove the efficiency of the method proposed

An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development

This final technical report describes the results of a NASA Innovative Advanced Concept (NIAC) Phase 2 study entitled "An Innovative Solution to NASA's NEO Impact Threat Mitigation Grand Challenge and Flight Validation Mission Architecture Development." This NIAC Phase 2 study was conducted at the Asteroid Deflection Research Center (ADRC) of Iowa State University in 2012-2014. The study objective was to develop an innovative yet practically implementable solution to the most probable impact threat of an asteroid or comet with short warning time (less than 5 years). The technical materials contained in this final report are based on numerous technical papers, which have been previously published by the project team of the NIAC Phase 1 and 2 studies during the past three years. Those technical papers as well as a NIAC Phase 2 Executive Summary report can be downloaded from the ADRC website (www.adrc.iastate.edu)