Show me the way to Monte Carlo: density-based trajectory navigation

We demonstrate the use of uncertain prediction in a system for pedestrian navigation via audio with a combination of Global Positioning System data, a music player, inertial sensing, magnetic bearing data and Monte Carlo sampling for a density following task, where a listener’s music is modulated according to the changing predictions of user position with respect to a target density, in this case a trajectory or path. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user and demonstrate that the system may be used effectively for varying trajectory width and context

CAN CHANGE PREDICTION HELP PRIORITISE REDESIGN WORK IN FUTURE ENGINEERING SYSTEMS?

Future design environments will necessitate improved management of the propagation and impacts of changes. To ascertain whether change prediction can assist in making better work prioritisation decisions, this paper develops a new simulation approach and applies it to a model of a complex aerospace product, which was elicited from industry. We use an accepted technique to generate potential change propagation trees and apply Monte Carlo methods to generate a sample space within which multiple scheduling policies could be evaluated and compared. The experiments reveal that poor coordination of change activity can result in significant process inefficiencies, that the potential for inefficiency increases for larger change networks, and that a modest ability to accurately predict change propagation in the specific case at hand could have a dramatic effect in reducing unnecessary rework. The experiments also suggest that the capability of predicting multiple steps of change propagation would provide only minimal additional improvement.International Design Conference - DESIGN 201

Monte Carlo Planning method estimates planning horizons during interactive social exchange

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference

Real Option Valuation of a Portfolio of Oil Projects

Various methodologies exist for valuing companies and their projects. We address the problem of valuing a portfolio of projects within companies that have infrequent, large and volatile cash flows. Examples of this type of company exist in oil exploration and development and we will use this example to illustrate our analysis throughout the thesis. The theoretical interest in this problem lies in modeling the sources of risk in the projects and their different interactions within each project. Initially we look at the advantages of real options analysis and compare this approach with more traditional valuation methods, highlighting strengths and weaknesses ofeach approach in the light ofthe thesis problem. We give the background to the stages in an oil exploration and development project and identify the main common sources of risk, for example commodity prices. We discuss the appropriate representation for oil prices; in short, do oil prices behave more like equities or more like interest rates? The appropriate representation is used to model oil price as a source ofrisk. A real option valuation model based on market uncertainty (in the form of oil price risk) and geological uncertainty (reserve volume uncertainty) is presented and tested for two different oil projects. Finally, a methodology to measure the inter-relationship between oil price and other sources of risk such as interest rates is proposed using copula methods.Imperial Users onl

Monte Carlo Planning method estimates planning horizons during interactive social exchange

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference