Co-evolving problems and solutions: The case of novice interaction designers in Botswana and the UK

This paper establishes that problem-solution co-evolution is observed in novice interaction designers in the UK and Botswana. However, in the majority of Botswana protocols we could see a new type of co-evolution, which we termed solution-problem co-evolution. Solution- problem co-evolution uses ‘off the shelf’’ solutions to structure the problem space. Both types of co-evolution are described and discussed in this paper. The findings are drawn from the analysis of 18 (5 UK, 13 Botswana) 1-hour design protocols from two cohorts of students studying the same undergraduate Open University Interaction Design module, one in Botswana and one in the UK. Participants were required to complete a medical interaction design task under controlled conditions. We based our analysis on a coding scheme that was developed specifically for this protocol study. The coding scheme is based on Schön’s seminal work on reflective practice. It visually represents activities in the problem and solutions spaces

Discursive design thinking: the role of explicit knowledge in creative architectural design reasoning

The main hypothesis investigated in this paper is based upon the suggestion that the discursive reasoning in architecture supported by an explicit knowledge of spatial configurations can enhance both design productivity and the intelligibility of design solutions. The study consists of an examination of an architect’s performance while solving intuitively a well-defined problem followed by an analysis of the spatial structure of their design solutions. One group of architects will attempt to solve the design problem logically, rationalizing their design decisions by implementing their explicit knowledge of spatial configurations. The other group will use an implicit form of such knowledge arising from their architectural education to reason about their design acts. An integrated model of protocol analysis combining linkography and macroscopic coding is used to analyze the design processes. The resulting design outcomes will be evaluated quantitatively in terms of their spatial configurations. The analysis appears to show that an explicit knowledge of the rules of spatial configurations, as possessed by the first group of architects can partially enhance their function-driven judgment producing permeable and well-structured spaces. These findings are particularly significant as they imply that an explicit rather than an implicit knowledge of the fundamental rules that make a layout possible can lead to a considerable improvement in both the design process and product. This suggests that by externalizing th

The Geometry of Monotone Operator Splitting Methods

We propose a geometric framework to describe and analyze a wide array of operator splitting methods for solving monotone inclusion problems. The initial inclusion problem, which typically involves several operators combined through monotonicity-preserving operations, is seldom solvable in its original form. We embed it in an auxiliary space, where it is associated with a surrogate monotone inclusion problem with a more tractable structure and which allows for easy recovery of solutions to the initial problem. The surrogate problem is solved by successive projections onto half-spaces containing its solution set. The outer approximation half-spaces are constructed by using the individual operators present in the model separately. This geometric framework is shown to encompass traditional methods as well as state-of-the-art asynchronous block-iterative algorithms, and its flexible structure provides a pattern to design new ones

Applications of Set-Theoretic Topology in the Construction and Analysis of Engineering Design Spaces

The idea of design spaces in engineering has appeared in many forms and served a variety of purposes in practice, research, and literature. Yet very few of the definitions put forth have a concrete mathematical structure that can be practically applied by the designer in real-time. This research seeks to address this gap by taking advantage of tools and techniques in point-set topology, a field that has been used successfully in a number of different areas. The primary objective of this undertaking is to formalize definitions for design spaces as topological structures that will encapsulate many of the relevant characteristics of both the problem to be solved and the designs that are being considered. Three separate spaces are presented: the problem space, the solution space, and the quality space. The problem space is defined by the requirements that pertain to the problem and represents the target that designs must hit to be considered a solution. The solution space is the collection of design embodiments for a given concept that meet the specified constraints. Finally, the objective space is a space that allows different design concepts to be compared to one another based on common criteria that any solution would exhibit. Along with these definitions, several methods are also proposed to operate on the design spaces to assist in their analysis and comparison. Measures are introduced for assessing the similarity of spaces as they evolve and for quantifying how sensitive solution spaces are to changes in requirements. Also, a process for gauging the relative utility of different concepts is presented. Two examples are included to demonstrate implementation, one simplistic for explanatory value and the second more complex to show scalability. Topology has been demonstrated to be a versatile and extensible lens for data interpretation and exploration. Given this adaptability, it is hoped that this thesis will serve as a foundation upon which future work can build so that a wide array of novel capabilities can be established for engineers, designers, researchers to draw upon in their pursuits

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Crematorium and Vertical Burial in Surabaya as Solution for Water Absorption in Urban Density Problem

Surabaya is the second largest city in Indonesia, which has many problems. Urban density is a major issue which has problems in it, starting from the population density up to the limitations of burial grounds that exist today. Design problem which arises is uniting the facility of funeral homes, crematoriums, and tombs in one area to reduce the mobilization on the road resulting in traffic density. Urban density has resulted in a couple of problems ranging from population density to scarcity of lands. Crematorium and vertical burial therefore become a crucial part of a city. While there has been less open green spaces, crematorium and vertical burial are expected not to reduce the open green spaces. Data scape as a design method is used to obtain design criteria for preliminary design. To obtain a form of structure, combination with the multiple addition geometry method is attempted. A design obtained by making vertical buriel footprint design that is able to provide sufficient absorption area by taking into account the percentage of urban open green space. To create a open green space and water absorption areas by lifting some mass of the building