Modeling the early stages of a user-centered process in architectural design through adaptation of the methodologies of New Product Design

In order to reach a degree of quality in architectural buildings that is likely to lead to user satisfaction, architectural design relies on integrating user-related information even before generation of building concepts. However, integrating such information may be seen as a hindrance to architectural creation. It therefore seems necessary to propose a methodological approach that allows integration of a user-centred point of view as well as generation of creative architectural concepts. Our research proposes to apply a collaborative process of New Product Design (NPD) in order to further enrich the traditional process of architectural design. We will present some experimental work carried out as part of an architectural project for the design of emergency shelters, as an alternative to more usual habitats. We will then discuss the possibility of adapting NPD methodology to architectural design, and what potential this offers to improve the integration of user-related information within architectural creativity

Learning robot policies using a high-level abstraction persona-behaviour simulator

2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCollecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients’ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user’s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user’s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user’s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.Peer ReviewedPostprint (author's final draft

Applying Design for Assembly Principles in Computer Aided Design to Make Small Changes that Improve the Efficiency of Manual Aircraft Systems Installations

The installation of essential systems into aircraft wings involves numerous labour-intensive processes. Many human operators are required to perform complex manual tasks over long periods of time in very challenging physical positions due to the limited access and confined space. This level of human activity in poor ergonomic conditions directly impacts on speed and quality of production but also, in the longer term, can cause costly human resource problems from operators' cumulative development of musculoskeletal injuries. These problems are exacerbated in areas of the wing which house multiple systems components because the volume of manual work and number of operators is higher but the available space is reduced.To improve the efficiency of manual work processes which cannot yet be automated we therefore need to consider how we might redesign systems installations in the enclosed wing environment to better enable operator access and reduce production time.This paper describes a recent study that applied design for assembly and maintainability principles and CATIA v5 computer aided design software to identify small design changes for wing systems installation tasks. Results show positive impacts for ergonomics, production time and cost, and maintainability, whilst accounting for aircraft performance and machining capabilities

Developing digital literacy in construction management education: a design thinking led approach

Alongside the digital innovations in AEC (Architectural, Engineering and Construction) practice, are calls for a new type of digital literacy, including a new information-based literacy informed by creativity, critical analysis and the theoretical and practical knowledge of the construction profession. This paper explores the role of design thinking and the promotion of abductive problem situations when developing digital literacies in construction education. The impacts of advanced digital modelling technologies on construction management practices and education are investigated before an examination of design thinking, the role of abductive reasoning and the rise of normative models of design thinking workflows. The paper then explores the role that design thinking can play in the development of new digital literacies in contemporary construction studies. A three-part framework for the implementation of a design thinking approach to construction is presented. The paper closes with a discussion of the importance of models of design thinking for learning and knowledge production, emphasising how construction management education can benefit from them

Systems, interactions and macrotheory

A significant proportion of early HCI research was guided by one very clear vision: that the existing theory base in psychology and cognitive science could be developed to yield engineering tools for use in the interdisciplinary context of HCI design. While interface technologies and heuristic methods for behavioral evaluation have rapidly advanced in both capability and breadth of application, progress toward deeper theory has been modest, and some now believe it to be unnecessary. A case is presented for developing new forms of theory, based around generic “systems of interactors.” An overlapping, layered structure of macro- and microtheories could then serve an explanatory role, and could also bind together contributions from the different disciplines. Novel routes to formalizing and applying such theories provide a host of interesting and tractable problems for future basic research in HCI

Bridging the gap Why we need to enhance current simulation models

Many models that simulate evacuations are state of the art and provide realistic insight to their users. However, simulating everyday situations, such as visitor flow through a museum or passenger flow through an airport, presents marked challenges; existing models reach their limit here. This contribution will introduce and highlight the gap between existing egress models and the difficulties found simulating, for instance, passenger flow or capacity analysis

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues