A conceptual framework for holistic assessment of decision support systems for sustainable livestock farming

The livestock sector has complex relationships with the three fundamental pillars of sustainability, i.e., environmental, economic, and social. Devising a livestock farming strategy by considering the different sustainability pillars is essential. Although several decision support systems (DSSs) are available for the livestock sector, these DSSs differ in the way they address sustainability. This work emphasizes the importance of a holistic approach to sustainable livestock management rather than only targeting individual sustainability dimensions. We, therefore, propose an initial assessment framework to evaluate the capacity of livestock DSSs in targeting the different sustainability pillars. In line with this, we present a conceptual basis for deriving assessment criteria and indicators. We then use the proposed assessment framework to assess existing openly available livestock DSSs. We observe that the main focus of the existing and openly available livestock-related DSSs is on the indicators from environmental pillars, and only a few of them accommodate economic aspects. No openly available DSS includes social and governance-related points. More importantly, none of these DSSs can handle data streams from Internet of Things (IoT) devices and, hence, they miss on the superiority that advanced modelling techniques can provide. With these observations, we draft an extensive set of guidelines for future livestock-related DSSs to holistically target sustainability

Metareasoning for Planning Under Uncertainty

The conventional model for online planning under uncertainty assumes that an agent can stop and plan without incurring costs for the time spent planning. However, planning time is not free in most real-world settings. For example, an autonomous drone is subject to nature's forces, like gravity, even while it thinks, and must either pay a price for counteracting these forces to stay in place, or grapple with the state change caused by acquiescing to them. Policy optimization in these settings requires metareasoning---a process that trades off the cost of planning and the potential policy improvement that can be achieved. We formalize and analyze the metareasoning problem for Markov Decision Processes (MDPs). Our work subsumes previously studied special cases of metareasoning and shows that in the general case, metareasoning is at most polynomially harder than solving MDPs with any given algorithm that disregards the cost of thinking. For reasons we discuss, optimal general metareasoning turns out to be impractical, motivating approximations. We present approximate metareasoning procedures which rely on special properties of the BRTDP planning algorithm and explore the effectiveness of our methods on a variety of problems.Comment: Extended version of IJCAI 2015 pape

Artificial Intelligence for Small Satellites Mission Autonomy

Space mission engineering has always been recognized as a very challenging and innovative branch of engineering: since the beginning of the space race, numerous milestones, key successes and failures, improvements, and connections with other engineering domains have been reached. Despite its relative young age, space engineering discipline has not gone through homogeneous times: alternation of leading nations, shifts in public and private interests, allocations of resources to different domains and goals are all examples of an intrinsic dynamism that characterized this discipline. The dynamism is even more striking in the last two decades, in which several factors contributed to the fervour of this period. Two of the most important ones were certainly the increased presence and push of the commercial and private sector and the overall intent of reducing the size of the spacecraft while maintaining comparable level of performances. A key example of the second driver is the introduction, in 1999, of a new category of space systems called CubeSats. Envisioned and designed to ease the access to space for universities, by standardizing the development of the spacecraft and by ensuring high probabilities of acceptance as piggyback customers in launches, the standard was quickly adopted not only by universities, but also by agencies and private companies. CubeSats turned out to be a disruptive innovation, and the space mission ecosystem was deeply changed by this. New mission concepts and architectures are being developed: CubeSats are now considered as secondary payloads of bigger missions, constellations are being deployed in Low Earth Orbit to perform observation missions to a performance level considered to be only achievable by traditional, fully-sized spacecraft. CubeSats, and more in general the small satellites technology, had to overcome important challenges in the last few years that were constraining and reducing the diffusion and adoption potential of smaller spacecraft for scientific and technology demonstration missions. Among these challenges were: the miniaturization of propulsion technologies, to enable concepts such as Rendezvous and Docking, or interplanetary missions; the improvement of telecommunication state of the art for small satellites, to enable the downlink to Earth of all the data acquired during the mission; and the miniaturization of scientific instruments, to be able to exploit CubeSats in more meaningful, scientific, ways. With the size reduction and with the consolidation of the technology, many aspects of a space mission are reduced in consequence: among these, costs, development and launch times can be cited. An important aspect that has not been demonstrated to scale accordingly is operations: even for small satellite missions, human operators and performant ground control centres are needed. In addition, with the possibility of having constellations or interplanetary distributed missions, a redesign of how operations are management is required, to cope with the innovation in space mission architectures. The present work has been carried out to address the issue of operations for small satellite missions. The thesis presents a research, carried out in several institutions (Politecnico di Torino, MIT, NASA JPL), aimed at improving the autonomy level of space missions, and in particular of small satellites. The key technology exploited in the research is Artificial Intelligence, a computer science branch that has gained extreme interest in research disciplines such as medicine, security, image recognition and language processing, and is currently making its way in space engineering as well. The thesis focuses on three topics, and three related applications have been developed and are here presented: autonomous operations by means of event detection algorithms, intelligent failure detection on small satellite actuator systems, and decision-making support thanks to intelligent tradespace exploration during the preliminary design of space missions. The Artificial Intelligent technologies explored are: Machine Learning, and in particular Neural Networks; Knowledge-based Systems, and in particular Fuzzy Logics; Evolutionary Algorithms, and in particular Genetic Algorithms. The thesis covers the domain (small satellites), the technology (Artificial Intelligence), the focus (mission autonomy) and presents three case studies, that demonstrate the feasibility of employing Artificial Intelligence to enhance how missions are currently operated and designed

Study of application of space telescope science operations software for SIRTF use

The design and development of the Space Telescope Science Operations Ground System (ST SOGS) was evaluated to compile a history of lessons learned that would benefit NASA's Space Infrared Telescope Facility (SIRTF). Forty-nine specific recommendations resulted and were categorized as follows: (1) requirements: a discussion of the content, timeliness and proper allocation of the system and segment requirements and the resulting impact on SOGS development; (2) science instruments: a consideration of the impact of the Science Instrument design and data streams on SOGS software; and (3) contract phasing: an analysis of the impact of beginning the various ST program segments at different times. Approximately half of the software design and source code might be useable for SIRTF. Transportability of this software requires, at minimum, a compatible DEC VAX-based architecture and VMS operating system, system support software similar to that developed for SOGS, and continued evolution of the SIRTF operations concept and requirements such that they remain compatible with ST SOGS operation

Science aspects of a 1980 flyby of Comet Encke with a Pioneer spacecraft

Results are presented of an investigation of the feasibility of a 1980 flyby of Comet Encke using a Pioneer class spacecraft. Specific areas studied include: science objectives and rationale; science observables; effects of encounter velocity; science encounter and targeting requirements; selection and description of science instruments; definition of a candidate science payload; engineering characteristics of suggested payload; value of a separable probe; science instruments for a separable probe; science payload integration problems; and science operations profile

A pilot level decision analysis of thermionic reactor development strategy for nuclear electric propulsion

The development policy for thermionic reactors to provide electric propulsion and power for space exploration was analyzed to develop a logical procedure for selecting development alternatives that reflect the technical feasibility, JPL/NASA project objectives, and the economic environment of the project. The partial evolution of a decision model from the underlying philosophy of decision analysis to a deterministic pilot phase is presented, and the general manner in which this decision model can be employed to examine propulsion development alternatives is illustrated

Assessing multiple sources of cadmium exposure in an Italian population.

Assessing multiple sources of cadmium exposure in an Italian population