Making a financial time machine:a multitouch application to enable interactive 3-D visualization of distant savings goals

Financial planning and decision making for the general public continues to vex and perplex in equal measure. Whilst the tools presented by a typical desktop computer should make the task easier, the recent financial crisis confirms the increasing difficulty that people have in calculating the benefits of deferring consumption for future gains (i.e. Saving). We present an interactive concept demonstration for Microsoft SurfaceTM that tackles two of the key barriers to saving decision making. Firstly we show an interface that avoid the laborious writing down or inputting of data and instead embodies the cognitive decision of allocation of resources in a physical gesture based interface, where the scale of the investment or expenditure correlates with the scale of the gesture. Second we show how a fast-forward based animation can demonstrate the impact of small increments in savings to a long term savings goal in a strategy game-based, interactive format. The platform uses custom software (XNATM format) as opposed to the more usual WPFTM format found on Surface applications. This enables dynamic 3-D graphical icons to be used to maximize the interactive appeal of the interface. Demonstration and test trial feedback indicates that this platform can be adapted to suit the narrative of individual purchasing decisions to inform educate diverse user groups about the long term consequences of small financial decisions

Model-based robocentric planning and navigation for dynamic environments

This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented

Optimising Flexibility of Temporal Problems with Uncertainty

Temporal networks have been applied in many autonomous systems. In real situations, we cannot ignore the uncertain factors when using those autonomous systems. Achieving robust schedules and temporal plans by optimising flexibility to tackle the uncertainty is the motivation of the thesis. This thesis focuses on the optimisation problems of temporal networks with uncertainty and controllable options in the field of Artificial Intelligence Planning and Scheduling. The goal of this thesis is to construct flexibility and robustness metrics for temporal networks under the constraints of different levels of controllability. Furthermore, optimising flexibility for temporal plans and schedules to achieve robust solutions with flexible executions. When solving temporal problems with uncertainty, postponing decisions according to the observations of uncertain events enables flexible strategies as the solutions instead of fixed schedules or plans. Among the three levels of controllability of the Simple Temporal Problem with Uncertainty (STPU), a problem is dynamically controllable if there is a successful dynamic strategy such that every decision in it is made according to the observations of past events. In the thesis, we make the following contributions. (1) We introduce an optimisation model for STPU based on the existing dynamic controllability checking algorithms. Some flexibility and robustness measures are introduced based on the model. (2) We extend the definition and verification algorithm of dynamic controllability to temporal problems with controllable discrete variables and uncertainty, which is called Controllable Conditional Temporal Problems with Uncertainty (CCTPU). An entirely dynamically controllable strategy of CCTPU consists of both temporal scheduling and variable assignments being dynamically decided, which maximize the flexibility of the execution. (3) We introduce optimisation models of CCTPU under fully dynamic controllability. The optimisation models aim to answer the questions how flexible, robust or controllable a schedule or temporal plan is. The experiments show that making decisions dynamically can achieve better objective values than doing statically. The thesis also contributes to the field of AI planning and scheduling by introducing robustness metrics of temporal networks, proposing an envelope-based algorithm that can check dynamic controllability of temporal networks with uncertainty and controllable discrete decisions, evaluating improvements from making decisions strongly controllable to temporally dynamically controllable and fully dynamically controllable and comparing the runtime of different implementations to present the scalability of dynamically controllable strategies

From systems to patterns and back - Exploring the spatial role of dynamic time and direction patterns in the area of regional planning

This master thesis presents a data-driven framework to explore the role of dynamic time and direction patterns in the area of Finnish Lapland in order to improve decision-making in urban planning and design tasks. The Arctic Ocean Railway project is chosen as a case study. In an era marked by dramatic environmental, political and societal changes, the Arctic region becomes more global and complex. An increasing number of actors are involved in its spatial transformations. Due to melting ice, the Northern Sea Route gains attention from the shipping and trade industries that are manifested in new port and infrastructure projects. Eco-tourism is booming in the Arctic due to its imaginary remoteness, while local Indigenous People try to preserve traditional livelihoods. In order to cope with the increasing complexity of such dynamic urban and regional challenges, Systems Thinking, dynamic patterns, modelling and use of simulation are researched to open up novel ways for complex regional planning methods. This is achieved by designing an agent-based model and using different representation and abstraction features for different dynamic data packages. The project is integrated within the GAMA simulation platform (a modelling and simulation development environment for building spatially explicit agent-based simulations) and embedded in the MIT CityScope framework - a medium for both, analyzing agent’s behavioural patterns and displaying them to the relevant stakeholders. The project attempts to address the necessity to handle the increasing complexity by presenting a dynamic, evidence-based planning and decision support tool called CityScope Lapland. The main goal of CityScope Lapland is to use digital technologies to incorporate variables like time and direction in urban spatial analysis and methodology; secondly, to improve the accessibility of the decision-making process for non-experts through a tangible user interface, and third, to help users evaluate their decisions by creating a feedback through real-time visualization of urban simulation results when facing less and less predictable futures. The project provides an alternative design approach, introducing new forms of urban imagination and different ways of perceiving and measuring complex spatial transformations

An Algorithm for Production Planning Based on Supply Chain KPIs

This paper observe multi-period multi-product production planning problem in make-to-stock production environment with limited production capacity. Such problem is identified in Fast Moving Consumer Goods industry. The goal was to develop an algorithm for supporting dynamic production triggering decisions in relation with two supply chain key performance indicators: stock cover and customer service level. The presented approach is applied to a real example in several scenarios based on different decision criteria