DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics

Integrating multicriteria decision analysis and scenario planning : review and extension

Scenario planning and multiple criteria decision analysis (MCDA) are two key management science tools used in strategic planning. In this paper, we explore the integration of these two approaches in a coherent manner, recognizing that each adds value to the implementation of the other. Various approaches that have been adopted for such integration are reviewed, with a primary focus on the process of constructing preferences both within and between scenarios. Biases that may be introduced by inappropriate assumptions during such processes are identified, and used to motivate a framework for integrating MCDA and scenario thinking, based on applying MCDA concepts across a range of "metacriteria" (combinations of scenarios and primary criteria). Within this framework, preferences according to each primary criterion can be expressed in the context of different scenarios. The paper concludes with a hypothetical but non-trivial example of agricultural policy planning in a developing country

Development of a project level performance measurement model for improving collaborative design team work

This research explored a new direction of improving collaborative design by performance measurement. More specifically, a novel 3-dimensional performance measurement model is developed and the purpose of this model is to help project managers improve team collaboration by indicating strengths and weaknesses of team members during the project development process. Considering the complexity of collaborative design work, a multiple criteria model is proposed to reflect the design dynamics, which highlights five performance indicators: efficiency, effectiveness, collaboration, management skills and innovation. These five indicators are mostly influenced by role-based performance measurement criteria (the second dimension). Design and development process (time) is also considered (the third dimension). This 3D model allows all involved design participants to measure work performance at any time during the product development process. In order to develop this model, the role-based task analysis and industrial survey methods were utilized. Three groups of role-based product design and development performance measurement criteria were identified for measuring design performance of the top managers, middle managers and individual designers in a project team. A 3-dimensional performance measurement method was proposed to calculate final performance scores from a performance measurement matrix. The proposed model was evaluated as a tool which can support project managers to reduce potential design and collaboration risks and increase confidence in decision-making process. The model has been discussed on implementing in a web-based application for measuring design performance throughout the product design and development proces

Environmentally Extended Input–Output Analysis of the UK Economy: Key Sector Analysis

The paper assesses the sustainability of investment in various economic sectors, with the aim of minimizing resource use and generation of emissions. The broad development focus of the paper and the potential for the proposed methodology to be applied in many different countries make it a useful methodological contribution to the global sustainability debate. The UK case is taken for illustration purposes, and (given the availability of the necessary data) this methodology could be applied in countries with various economic structures and specialisations. An environmentally extended static 123-sector UK input–output model is used, linking a range of physical flows (domestic extraction, use of water, and emissions of CO2, CH4, NOx) with the economic structure of the UK. A range of environmentally adjusted forward and backward linkage coefficients has been developed, adjusted according to final demand, domestic extraction, publicly supplied and directly abstracted water, amd emissions of CO2 and NOx,. The data on the final demandadjusted and environmentally adjusted forward and backward linkage coefficients were used in a multi-criteria decision-aid assessment, employing a NAIADE method in three different sustainability settings. The assessment was constructed in such a way that each sector of the UK economy was assessed by means of a panel of sustainability criteria, maximizing economic effects and minimizing environmental effects. This type of multi-criteria analysis, applied here for the first time, could prove to be a valuable basis for similar studies, especially in the developing world, where trade-offs between economic development and environmental protection have been the subject of considerable debate.input–output analysis; environmentally extended; MCDA; key sectors; sustainability; ecological economics; UK