Question answering over knowledge graphs for explainable satellite scheduling

Schedules for satellitemissions consist of thousands, if not millions, of interconnected activities executing many times across days,months, and years to fulfill mission objectives. The complexity of a schedule can make it difficult for Ground Station Operators (GSO) to understand the relationship between activities as part of a complete mission, especially when schedules have been created by means of an autonomous decision making algorithm. Text-based explanations are helpful in establishing the reasoning behind decisions suggested by algorithms and their impact on the overall execution plan. A Knowledge Graph (KG) can provide the underlying data structure to record what has happened and what is scheduled, as well as the interconnected elements that are impacted by the scheduled activities. The relationship between satellite components, environmental conditions, operational constraints, and mission objectives is complex and highly dimensional, which is not easy for a single operator to manage concurrently. A system that can gather information from a KG, and infer the information stored within, can assist human operators in building a deeper understanding of the relationships of automatically scheduled decisions. A natural language query interface to the KG is the simplest way for a human to interface and extract knowledge. Additionally,manual access to the KG can be provided alongside textual answers, enabling exploration of schedule branches to understand what else can change throughout the mission’s execution. This improves the robustness of a system’s responses to queries and allows for greater flexibility. An overview is therefore examined of how KG and Natural Language Processing (NLP) technologies can be used to facilitate eXplainable Artificial Intelligence (XAI) in satellite scheduling. Namely, how to model the schedule and environment information to be stored in the graph and how to reason over such information by interpreting user queries in natural language. An example is presented demonstrating the capabilities of flexible query interpretation on an Earth Observation (EO) satellite scheduling problem. Finally, the capabilities of KG and NLP technologies to provide more explainable insights into satellite scheduling tasks are discussed in the frame of future possible developments

On-board re-planning of an earth observation satellite for maximisation of observation campaign goals

Earth Observation (EO) Satellite task planning entails careful temporal consideration of actions depending on the assigned mission goals required for scheduling. Mid term plans are derived on ground and uploaded to the spacecraft for execution. However once in orbit, to maximise scientific mission return, the satellite needs to have autonomous re-planning capabilities to account for unforeseen events. This compensates for the reliance of communication with the ground stations, especially due to the limited frequency of transmission. In the specific case of EO satellites, experienced uncertainties can be due to environmental or observational conditions, which can affect the optimal execution of mid term plans. Autonomous on-board re-planning ensures the maximisation of the observation campaign goals within the problem constraints. An autonomous recovery algorithm via a Stochastic Problem (SP) implemented through the generation of a model required for on-board re-planning of actions to reduce dependency on human interaction. This is to attain updates for an executable plan to maximise observation campaign mission goals. The updated decisions and data related to environment and operations, are used to provide explanations to ground operators, enabling a human understanding of the actions taken autonomously by the system on-board

Abstract argumentation for explainable satellite scheduling

Satellite schedules are derived from satellite mission objectives, which are mostly managed manually from the ground. This increases the need to develop autonomous on-board schedul- ing capabilities and reduce the requirement for manual manage- ment of satellite schedules. Additionally, this allows the unlocking of more capabilities on-board for decision-making, leading to an optimal campaign. However, there remain trust issues in decisions made by Artificial Intelligence (AI) systems, especially in risk-averse environments, such as satellite operations. Thus, an explanation layer is required to assist operators in understanding decisions made, or planned, autonomously on-board. To this aim, a satellite scheduling problem is formulated, utilizing real world data, where the total number of actions are maximised based on the environmental constraints that limit observation and down-link capabilities. The formulated optimisation problem is solved with a Constraint Programming (CP) method. Later, the mathematical derivation for an Abstract Argumentation Framework (AAF) for the test case is provided. This is proposed as the solution to provide an explanation layer to the autonomous decision-making system. The effectiveness of the defined AAF layer is proven on the daily schedule of an Earth Observation (EO) mission, monitoring land surfaces, demonstrating greater capabilities and flexibility, for a human operator to inspect the machine provided solution

Constraint programming for scheduling the operations of STRATHcube : a nanosatellite for detecting space debris

The ever-increasing quantity of satellites and space debris in orbit pose a serious threat to the sustainable use of the space environment. To mitigate this threat, we must improve our detection and tracking space debris in low earth orbit, and to do this new space-based tracking methods will be required. Subsequently, it raises the need to optimise the schedules of these in orbit tracking satellites to maximise the number and accuracy of the debris detected. STRATHcube is a nanosatellite currently in development at the University of Strathclyde that will use passive bi-static RADAR to detect space debris and act as a technological demonstrator. This satellite will be used to exhibit the space debris tracking technology and will use the iridium constellation as an illuminator. However, the complex interplay of satellite positions, with respect to the illuminator constellation and the ground stations, makes scheduling operations of the satellite very complex and difficult for a human to compute without the aid of automatic solvers. The whole space industry is moving towards developing more autonomy on-board satellites, also related to on-board task management. Constraint programming is the technique used to schedule STRATHcube tasks by optimising RADAR detections, ground station communications, and on-board data handling. This was done by mathematically defining the constraints on the satellite, simulating periods of the mission to find relevant orbital and space environment data. These were then used to manually define a baseline schedule, which was used as a starting point for the constraint’s optimisation search. The optimised schedule significantly improved the satellite operations compared to the manually designed one. The improvements in scheduling will be applied to STRATHcube to improve its operations and allow it to better demonstrate the use of passive bi-static RADAR for space debris detection. The optimisation methods could also be applied to future possible passive bi-static RADAR satellites to maximise their efficiency in operations

Natural language processing for explainable satellite scheduling

Facilitating the interactions between humans and Artificial Intelligence (AI) in automated systems is becoming central with the advancements in technology and their more widespread adoption in practical applications. Mathematical programming scheduling techniques are a driving factor to assist ground station operators both on board the satellite, for autonomous decision making, and on ground, for supporting mid-term operations scheduling. When communication to ground is limited, scheduling algorithms require a level of autonomy and robustness able to respond to issues arising on board the satellite in the absence of communication with a ground operator. Moreover, explanations must be generated, along side schedules, for the operator to build and gain trust in the autonomous system. Explainable Artificial Intelligence (XAI) is an emerging topic in AI. Explanations are a necessary layer to effectively deploy autonomous trustworthy systems in practical applications. Queries may arise from operators such as why, what, when and how the scheduled actions were selected autonomously on board for a specific time. Explanations are provided based on the definition of the problem with its respective constraints. Autonomous decision making algorithms can be explained in several ways. Computational Argumentation (CA) and Natural Language Processing (NLP)) are some techniques, belonging to the domains of formal logic and machine learning, that can be used to generate explanations and communicate them back to the user in the form of textual output. An Argumentation Framework (AF) was created to assist in answering questions raised by the end user. The AF encodes, in its lower level, all the necessary information on when conflicts may occur between actions, as well as, environmental conditions inhibiting the occurrence of the actions within a schedule. This database of information is used to construct arguments in support or negation of user submitted queries or to provide an explanation of the complete derived schedule. NLP is then used as a bridge to communicate the relevant arguments to the user. The queries received revolved around three main areas: the subject, the time of interest and the intent. Following the interpretation, the queries were mapped to the AF database, returning a list of conflicts, agreements and neutral outcomes. The chosen NLP method for this architecture, GPT-3 was used to then deduce the answer to the query and justify it with a textual explanation

Convolutional Generative Adversarial Network, via Transfer Learning, for traditional Scottish music generation

The concept of a Binary Multi-track Sequential Generative Adversarial Network (BinaryMuseGAN) used for the generation of music has been applied and tested for various types of music. However, the concept is yet to be tested on more specific genres of music such as traditional Scottish music, for which extensive collections are not readily available. Hence exploring the capabilities of a Transfer Learning (TL) approach on these types of music is an interesting challenge for the methodology. The curated set of MIDI Scottish melodies was preprocessed in order to obtain the same number of tracks used in the BinaryMuseGAN model; converted into pianoroll format and then used as training set to fine tune a pretrained model, generated from the Lakh MIDI dataset. The results obtained have been compared with the results obtained by training the same GAN model from scratch on the sole Scottish music dataset. Results are presented in terms of variation and average performances achieved at different epochs for five performance metrics, three adopted from the Lakh dataset (qualified note rate, polyphonicity, tonal distance) and two custom defined to highlight Scottish music characteristics (dotted rhythm and pentatonic note). From these results, the TL method shows to be more effective, with lower number of epochs, to converge stably and closely to the original dataset reference metrics values

The unforgiven: Pathways to homelessness

honors thesisCollege of Social & Behavioral ScienceSociologyHeather MeltonHomelessness is a socially constructed problem. We as a society do not take the time to truly listen to the concerns of those who are still impact us in our day to day lives. In this current study, the use of qualitative data was analyzed to determine and understand pathways to homelessness. The purpose is to develop an understanding of why people may become chronically homeless. People who are in a constant cycle of homelessness exhibit constants that may cause them to end up homeless time and time again. Through the process of this research, I gained an insight into their lives and an understanding of why they may end up in this cycle of homelessness. With further knowledge about the homeless and the issues they deal with, agencies that provide for this population can better shape their programs to fit the needs of this population

Towards explainability of on-board satellite scheduling for end user interactions

Satellite scheduling is a requirement for automating routines and tasks prior to execution on given satellite/s. Various techniques and tools are used with the optional incorporation of AI depending on the schedule constraints and available resources for memory allocation. Regardless of the technique and approach taken, most autonomous scheduling systems experience challenges enabling an interaction between the user and the system. This affects trust in the system that can lead to manual handling of data that wastes time and resources. Therefore, to reduce these situations from occurring and save costs, the user needs explanations on decisions made autonomously by the system. An optimal scheduling approach was taken with the use of Constraint Programming (CP) for allocating on-board tasks for a single satellite's schedule. A schedule was derived for short-term planning where tasks were evaluated on duration, cost and resource requirements These results were analysed for their feasibility and optimality; and in doing so, an Computational Argumentation (CA) layer was developed to provide explanations on whether the tasks scheduled, supported, or conflicted with the temporal and/or resource constraints. To depict the stages and relationships of these internal arguments, an entity relationship graph was created containing the proposed schedule solutions that were evaluated based on their corresponding conflicts/agreements. Due to the nature of these arguments and their respective constraints, another argumentation approach was used to derive basic causalities to provide information on reason of failure and impact on schedule. For end user interactions, the design of explanation layer was investigated allowing the user to select different parts of the proposed schedule enabling a basic output description displayed to assist and enhance the users understanding. This approach will also give the user the possibility to propose changes in the solution and evaluate its feasibility/optimality as well as deriving conflicts with the current schedule. This will allow for growth to build more advanced explainable techniques for sophisticated and complex schedules

Data from: Genomic differentiation during speciation-with-gene-flow: comparing geographic and host-related variation in divergent life history adaptation in Rhagoletis pomonella

A major goal of evolutionary biology is to understand how variation within populations gets partitioned into differences between reproductively isolated species. Here, we examine the degree to which diapause life history timing, a critical adaptation promoting population divergence, explains geographic and host-related genetic variation in ancestral hawthorn and recently derived apple-infesting races of Rhagoletis pomonella. Our strategy involved combining experiments on two different aspects of diapause (initial diapause intensity and adult eclosion time) with a geographic survey of genomic variation across four sites where apple and hawthorn flies co-occur from north to south in the Midwestern USA. The results demonstrated that the majority of the genome showing significant geographic and host-related variation can be accounted for by initial diapause intensity and eclosion time. Local genomic differences between sympatric apple and hawthorn flies were subsumed within broader geographic clines; allele frequency differences within the races across the Midwest were 2 to 3-fold greater than those between the races in sympatry. As a result, sympatric apple and hawthorn populations displayed more limited genomic clustering compared to geographic populations within the races. The findings suggest that with reduced gene flow and increased selection on diapause equivalent to that seen between geographic sites, the host races may be recognized as different genotypic entities in sympatry, and perhaps species, a hypothesis requiring future genomic analysis of related sibling species to R. pomonella to test. Our findings concerning the way selection and geography interplay could be of broad significance for many cases of earlier stages of divergence-with-gene flow, including (1) where only modest increases in geographic isolation and the strength of selection may greatly impact genetic coupling and (2) the dynamics of how spatial and temporal standing variation is extracted by selection to generate differences between new and discrete units of biodiversity

GrantApple.fq.gz

Fastq file containing demultiplexed sequences for the control (7 day) samples from the apple selection experiment