Delaying the Catastrophic Arrival of the Brown Tree Snake to Hawaii

This paper develops a two-stage model for the optimal management of a potential invasive species. The arrival of an invasive species is modeled as an irreversible event with an uncertain arrival time. The model is solved in two stages, beginning with the post-invasion stage. In this stage, we assume perfect certainty regarding population size and arrivals. The loss-minimizing paths of prevention and control are identified, resulting in a minimized present value penalty associated with the invasion. After calculating this penalty, we analyze the pre-invasion stage and solve for the level of prevention expenditures that will minimize expected total cost. For the case of the Brown Tree Snake potentially invading Hawaii, we find that under a regime of precommitment, pre-invasion expenditures on prevention should be approximately $3.2 million today, decreasing every year until invasion. However, if the planner is permitted to re-evaluate the threat following a non-event, prevention will be lower ($2.96 million a year) and constant until invasion. Once invasion occurs, optimal management requires lower annual expenditures on prevention ($3.1 million) but requires$1.6 million to be spent on control annually to keep the population at its steady state level.catastrophe, hazard function, invasive species, Brown Tree Snake, Boiga irregularis, prevention and control, Hawaii

Planning and scheduling research at NASA Ames Research Center

Planning and scheduling is the area of artificial intelligence research that focuses on the determination of a series of operations to achieve some set of (possibly) interacting goals and the placement of those operations in a timeline that allows them to be accomplished given available resources. Work in this area at the NASA Ames Research Center ranging from basic research in constrain-based reasoning and machine learning, to the development of efficient scheduling tools, to the application of such tools to complex agency problems is described

Identifying Conflicting Requirements in Systems of Systems

A System of Systems (SoS) is an arrangement of useful and independent sub-systems, which are integrated into a larger system. Examples are found in transport systems, nutritional systems, smart homes and smart cities. The composition of component sub-systems into an SoS enables support for complex functionalities that cannot be provided by individual sub-systems on their own. However, to realize the benefits of these functionalities it is necessary to address several software engineering challenges including, but not limited to, the specification, design, construction, deployment, and management of an SoS. The various component sub-systems in an SoS environment are often concerned with distinct domains; are developed by different stake-holders under different circumstances and time; provide distinct functionalities; and are used by different stakeholders, which allow for the existence of conflicting requirements. In this paper, we present a framework to support management of emerging conflicting requirements in an SoS. In particular, we describe an approach to support identification of conflicts between resource-based requirements (i.e. requirements concerned with the consumption of different resources). In order to illustrate and evaluate the work, we use an example of a pilot study of an IoT SoS ecosystem designed to support food security at different levels of granularity, namely individuals, groups, cities, and nations

Domain independent goal recognition

Goal recognition is generally considered to follow plan recognition. The plan recognition problem is typically defined to be that of identifying which plan in a given library of plans is being executed, given a sequence of observed actions. Once a plan has been identified, the goal of the plan can be assumed to follow. In this work, we address the problem of goal recognition directly, without assuming a plan library. Instead, we start with a domain description, just as is used for plan construction, and a sequence of action observations. The task, then, is to identify which possible goal state is the ultimate destination of the trajectory being observed. We present a formalisation of the problem and motivate its interest, before describing some simplifying assumptions we have made to arrive at a first implementation of a goal recognition system, AUTOGRAPH. We discuss the techniques employed in AUTOGRAPH to arrive at a tractable approximation of the goal recognition problem and show results for the system we have implemented