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Communications of the ACM

81 - 90 of 3,913 for bentley

Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.


Do Game Bots Dream of Electric Rewards?: The universality of intrinsic motivation

The purpose of this paper is to draw together theories, ideas, and observations related to rewards, motivation, and play to develop and question our understanding and practice of designing reward-based systems and technology. Our exploration includes reinforcement, rewards, motivational theory, flow, play, games, gamification, and machine learning. We examine the design and psychology of reward-based systems in society and technology, using gamification and machine learning as case studies. We propose that the problems that exist with reward-based systems in our society are also present and pertinent when designing technology. We suggest that motivation, exploration, and play are not just fundamental to human learning and behaviour, but that they could transcend nature into machine learning. Finally, we question the value and potential harm of the reward-based systems that permeate every aspect of our lives and assert the importance of ethics in the design of all systems and technology.


Explaining factors affecting telework adoption in South African organisations pre-COVID-19

The COVID-19 pandemic of 2020 saw governments across the world mandating telework for entire populations thereby bringing the topic of telework into sharp focus. Telework is a well-researched topic which dates as far back as five decades ago. While telework provides many indisputable benefits to organisations, society and individuals, it has not achieved the anticipated widespread adoption. While telework studies have examined multiple aspects, few studies have examined organisational factors which affect telework adoption. This study is an empirical investigation of telework adoption, using a set of factors identified in the literature in organisations in a South African context. These factors in prior studies were found to enable or prevent an organisation from adopting telework. The question thus asked in this study was “Which factors enable or prevent the adoption of telework within South African organisations?” A survey with 104 valid responses was analysed using Statistica. The theoretical contribution of the study is a validated model of factors influencing the adoption of telework.


Adaptive Weighted Finite Mixture Model: Identifying the Feature-Influence of Real Estate

It is significant for real estate investors to understand how the construction environments and building characteristics impact the housing unit price. However, it is challenging for identifying the complex feature-influence from construction environments and building characteristics. It is also hard to alleviate the heterogeneity of real estate. In this article, we propose a framework named Adaptive Weighted Finite Mixture Model to identify the feature-influence and simultaneously alleviate the ill effect of heterogeneity. Applying this framework, we can predict the housing unit price based on the corresponding features. Besides, we discover that the feature-influence exists in the dissimilarity among similar cities. Specifically, we adaptively learn the weights of features to identify the feature-influence, and we model the estimation of the housing unit price with the feature-influence into a finite mixture model. We utilize the Principle Component Analysis algorithm to obtain a low-dimensional representation of housing features. The low-dimensional representation reduces the computational cost of model learning, and it avoids a potential catastrophe of the singular matrix inversion during the process of learning model parameters, which are estimated by the Expectation Maximization algorithm. To avoid the blind search for the latent group number used in the proposed framework, we employ the pre-clustering result as a guide of the searching range of the latent group numbers. We conduct numerous experiments on three real datasets from Shenyang, Changchun, and Harbin, which are the three provincial capital cities that have similar geography, economics, and cultures. The experimental results demonstrate the effectiveness of the proposed framework.


"Miss understandable": a study on how users appropriate voice assistants and deal with misunderstandings

This study examines the appropriation and usage of voice assistants like Google Assistant or Amazon Alexa in private households. Our research is based on 10 in-depth interviews with users of voice assistants and a follow-up evaluation of their interaction histories. Our results illustrate situations in which the voice assistants were used at home, what strategies the users adopted to interact with them, how the interactions were performed, and what difficulties users experienced. A focus of our study is on misunderstandings, situations where interactions failed partially or completely. Our study shows that the full potential of voice assistants is often not utilized, as complex interactions are often subject to failures and users revert to simple use cases. Our participants used their voice assistant mostly for simple commands, often not even trying new functions. An analysis of their appropriation strategies resulted in implications for the design of supportive tools as well as the further development and optimization of voice interfaces.


(Non-)Interacting with conversational agents: perceptions and motivations of using chatbots and voice assistants

Conversational agents (CAs) such as Siri, Alexa, and Google Assistant are increasingly penetrating everyday life. From a Human-Computer Interaction (HCI) perspective, designing CAs that appropriately support the way they are used within daily life is still challenging. While initial design guidelines for human-AI interaction exist, we still know little about how users actually perceive CAs within their daily lives and what aspects motivate their usage of such tools. Within our research, we therefore conducted an interview study with 29 participants to uncover daily positive and negative experiences with CAs. By revealing how users currently perceive CAs, we identify quality criteria that could inform their future design. By evaluating these criteria with respect to existing research discourses about user experience (UX) guidelines for CAs, we contribute to the field by extending these guidelines from an end-user's perspective.


What If Conversational Agents Became Invisible?: Comparing Users' Mental Models According to Physical Entity of AI Speaker

The popularity of conversational agents (CAs) in the form of AI speakers that support ubiquitous smart homes has increased because of their seamless interaction. However, recent studies have revealed that the use of AI speakers decreases over time, which shows that current agents do not fully support smart homes. Because of this problem, the possibility of unobtrusive, invisible intelligence without a physical device has been suggested. To explore CA design direction that enhances the user experience in smart homes, we aimed to understand each feature by comparing an invisible agent with visible ones embedded in stand-alone AI speakers. We conducted a drawing study to examine users' mental models formed through communicating with two different physical entities (i.e., visible and invisible CAs). From the drawings, interviews, and surveys, we identified how users' mental models and interactions differed depending on the presence of a physical entity. We found that a physical entity affected users' perceptions, expectations, and interactions toward the agent.


Hello There! Is Now a Good Time to Talk?: Opportune Moments for Proactive Interactions with Smart Speakers

Increasing number of researchers and designers are envisioning a wide range of novel proactive conversational services for smart speakers such as context-aware reminders and restocking household items. When initiating conversational interactions proactively, smart speakers need to consider users' contexts to minimize disruption. In this work, we aim to broaden our understanding of opportune moments for proactive conversational interactions in domestic contexts. Toward this goal, we built a voice-based experience sampling device and conducted a one-week field study with 40 participants living in university dormitories. From 3,572 in-situ user experience reports, we proposed 19 activity categories to investigate contextual factors related to interruptibility. Our data analysis results show that the key determinants for opportune moments are closely related to both personal contextual factors such as busyness, mood, and resource conflicts for dual-tasking, and the other contextual factors associated with the everyday routines at home, including user mobility and social presence. Based on these findings, we discuss the need for designing context-aware proactive conversation management features that dynamically control conversational interactions based on users' contexts and routines.


DeltaPQ: lossless product quantization code compression for high dimensional similarity search

High dimensional data is ubiquitous and plays an important role in many applications. However, the size of high dimensional data is usually excessively large. To alleviate this problem, in this paper, we develop novel techniques to compress and search high dimensional data. Specifically, we first apply vector quantization, a classical lossy data compression method. It quantizes a high dimensional vector to a sequence of small integers, namely the quantization code. Then, we propose a novel lossless compression algorithm, DeltaPQ, to further compress the quantization codes. DeltaPQ organizes the quantization codes in a tree structure and stores the differences between two quantization codes rather than the original codes. Among the exponential number of possible tree structures, we develop an efficient algorithm, whose time and space complexity are linear to the number of codes, to find the one with optimal compression ratio. The approximate nearest neighbor search query can be processed directly on the compressed data with small space overhead in a few bytes. Many similarity measures can be supported, such as inner product, cosine similarity, Euclidean distance, and Lp-norm. Experimental results on five large-scale real-world datasets show that DeltaPQ achieves a compression ratio of up to 5 (and often greater than 2) on the quantization codes whereas the state-of-art general-purpose lossless compression algorithms barely work.