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Articles 1621 - 1650 of 3560

Full-Text Articles in Databases and Information Systems

The Impact Of Rapid Release Cycles On The Integration Delay Of Fixed Issues, Daniel Alencar Da Costa, Shane Mcintosh, Christoph Treude, Uirá Kulesza, Ahmed E. Hassan Apr 2018

The Impact Of Rapid Release Cycles On The Integration Delay Of Fixed Issues, Daniel Alencar Da Costa, Shane Mcintosh, Christoph Treude, Uirá Kulesza, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

The release frequency of software projects has increased in recent years. Adopters of so-called rapid releases—short release cycles, often on the order of weeks, days, or even hours—claim that they can deliver fixed issues (i.e., implemented bug fixes and new features) to users more quickly. However, there is little empirical evidence to support these claims. In fact, our prior work shows that code integration phases may introduce delays for rapidly releasing projects—98% of the fixed issues in the rapidly releasing Firefox project had their integration delayed by at least one release. To better understand the impact that rapid release cycles …

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Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim Apr 2018

Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely …

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Eat & Tell: A Randomized Trial Of Random-Loss Incentive To Increase Dietary Self-Tracking Compliance, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek, Tianjiao Yun Apr 2018

Eat & Tell: A Randomized Trial Of Random-Loss Incentive To Increase Dietary Self-Tracking Compliance, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek, Tianjiao Yun

Research Collection School Of Computing and Information Systems

A growing body of evidence has shown that incorporating behavioral economics principles into the design of financial incentive programs helps improve their cost-effectiveness, promote individuals' short-term engagement, and increase compliance in health behavior interventions. Yet, their effects on long-term engagement have not been fully examined. In study designs where repeated administration of incentives is required to ensure the regularity of behaviors, the effectiveness of subsequent incentives may decrease as a result of the law of diminishing marginal utility. In this paper, we introduce random-loss incentive-a new financial incentive based on loss aversion and unpredictability principles-to address the problem of individuals' …

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A Feasibility Study On Crowdsourcing To Monitor Municipal Resources In Smart Cities, Thivya Kandappu, Archan Misra, Ming Hui, Desmond (Xu Minghui) Koh, Randy Tandriansyah Daratan, Nikita Jaiman Apr 2018

A Feasibility Study On Crowdsourcing To Monitor Municipal Resources In Smart Cities, Thivya Kandappu, Archan Misra, Ming Hui, Desmond (Xu Minghui) Koh, Randy Tandriansyah Daratan, Nikita Jaiman

Research Collection School Of Computing and Information Systems

Active citizenry, whereby citizens actively participate inreporting and addressing challenges in urban service delivery is a strategic goalof smart cities such as Singapore. In spite of the promise, we believe that thesuccess of such large-scale nation-wide crowdsourcing deployments depend on thereal-word user preferences and behavioral characteristics of citizens. In thispaper, we first present our findings on behavioral preferences and key concernsof citizens regarding smart-city services via an opinion survey conducted with 1300participants. We then propose a “citizen-controlled” urban services reportingplatform where citizens actively report on the status of various municipalresources. We advocate the importance of matching user mobility patternsagainst task …

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Scalable Hypergraph-Based Image Retrieval And Tagging System, Lu Chen, Yunjun Gao, Yuanliang Zhang, Sibo Wang, Baihua Zheng Apr 2018

Scalable Hypergraph-Based Image Retrieval And Tagging System, Lu Chen, Yunjun Gao, Yuanliang Zhang, Sibo Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Massive amounts of images textually annotated by different users are provided by social image websites, e.g., Flickr. Social images are always associated with various information, such as visual features, tags, and users. In this paper, we utilize hypergraph instead of ordinary graph to model social images, since relations among various information are more sophisticated than pairwise. Based on the hypergraph, we propose HIRT, a scalable image retrieval and tagging system, which uses Personalized PageRank to measure vertex similarity, and employs top-k search to support image retrieval and tagging. To achieve good scalability and efficiency, we develop parallel and approximate top-k …

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Combination Forecasting Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Shunchang Yu, Bin Li, Steven C. H. Hoi, Shuigeng G. Zhou Apr 2018

Combination Forecasting Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Shunchang Yu, Bin Li, Steven C. H. Hoi, Shuigeng G. Zhou

Research Collection School Of Computing and Information Systems

Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, …

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Location-Aware Influence Maximization Over Dynamic Social Streams, Yanhao Wang, Yuchen Li, Ju Fan, Kianlee Tan Apr 2018

Location-Aware Influence Maximization Over Dynamic Social Streams, Yanhao Wang, Yuchen Li, Ju Fan, Kianlee Tan

Research Collection School Of Computing and Information Systems

Influence maximization (IM), which selects a set of k seed users (a.k.a., a seed set) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications. However, most existing IM algorithms are static and location-unaware. They fail to provide high-quality seed sets efficiently when the social network evolves rapidly and IM queries are location-aware. In this article, we first define two IM queries, namely Stream Influence Maximization (SIM) and Location-aware SIM (LSIM), to track influential users over social streams. Technically, SIM adopts the sliding window model and maintains a seed set with …

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Continuous Top-K Monitoring On Document Streams (Extended Abstract), Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li Apr 2018

Continuous Top-K Monitoring On Document Streams (Extended Abstract), Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Research Collection School Of Computing and Information Systems

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The objective is to support large numbers of users and high stream rates, while refreshing the topk results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach, …

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A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham Apr 2018

A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, …

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The Role Of Urban Mobility In Retail Business Survival, Krittika D'Silva, Kasthuri Jayarajah, Anastasios Noulas, Cecilia Mascolo, Archan Misra Apr 2018

The Role Of Urban Mobility In Retail Business Survival, Krittika D'Silva, Kasthuri Jayarajah, Anastasios Noulas, Cecilia Mascolo, Archan Misra

Research Collection School Of Computing and Information Systems

Economic and urban planning agencies have strong interest in tackling the hard problem of predicting the odds of survival of individual retail businesses. In this work, we tap urban mobility data available both from a location-based intelligence platform, Foursquare, and from public transportation agencies, and investigate whether mobility-derived features can help foretell the failure of such retail businesses, over a 6 month horizon, across 10 distinct cities spanning the globe. We hypothesise that the survival of such a retail outlet is correlated with not only venue-specific characteristics but also broader neighbourhood-level effects. Through careful statistical analysis of Foursquare and taxi …

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Social Network Monitoring For Bursty Cascade Detection, Wei Xie, Feida Zhu, Jing Xiao, Jianzong Wang Apr 2018

Social Network Monitoring For Bursty Cascade Detection, Wei Xie, Feida Zhu, Jing Xiao, Jianzong Wang

Research Collection School Of Computing and Information Systems

Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we …

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Detect Rumor And Stance Jointly By Neural Multi-Task Learning, Jing Ma, Wei Gao, Kam-Fai Wong Apr 2018

Detect Rumor And Stance Jointly By Neural Multi-Task Learning, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

In recent years, an unhealthy phenomenon characterized as the massive spread of fake news or unverified information (i.e., rumors) has become increasingly a daunting issue in human society. The rumors commonly originate from social media outlets, primarily microblogging platforms, being viral afterwards by the wild, willful propagation via a large number of participants. It is observed that rumorous posts often trigger versatile, mostly controversial stances among participating users. Thus, determining the stances on the posts in question can be pertinent to the successful detection of rumors, and vice versa. Existing studies, however, mainly regard rumor detection and stance classification as …

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Pccf: Periodic And Continual Temporal Co-Factorization For Recommender Systems, Guibing Guo, Feida Zhu, Shilin Qu, Xingwei Wang Apr 2018

Pccf: Periodic And Continual Temporal Co-Factorization For Recommender Systems, Guibing Guo, Feida Zhu, Shilin Qu, Xingwei Wang

Research Collection School Of Computing and Information Systems

Rating-only collaborative filtering has been extensively studied for decades with great improvements achieved in predicting a user’s preference on a target item at a particular time point. Yet, it remains a research challenge on how to capture users’ rating patterns which may drift over time. In this article, we propose a time-aware matrix co-factorization model, called PCCF, which considers two types of temporal effects, i.e., periodic and continual. Specifically, periodic effects refer to the impact of discrete periodic time slices with which users’ preferences may be associated, and continual effects refer to the impact of continuous gradual time over which …

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Persona Perception Scale: Developing And Validating An Instrument For Human-Like Representations Of Data, Salminen Joni, Haewoon Kwak, João Santos, Soon-Gyo Jung, Jisun An, Bernard J. Jansen Apr 2018

Persona Perception Scale: Developing And Validating An Instrument For Human-Like Representations Of Data, Salminen Joni, Haewoon Kwak, João Santos, Soon-Gyo Jung, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Personas are widely used in software development, system design, and HCI studies. Yet, their evaluation is difficult, and there are no recognized and validated measurement scales to date. To improve this condition, this research develops a persona perception scale based on reviewing relevant literature. We validate the scale through a pilot study with 19 participants, each evaluating three personas (57 evaluations in total). This is the first reported effort to systematically develop and validate an instrument for persona perception measurement. We find the constructs and items of the scale perform well, with factor loadings ranging between 0.60 and 0.95. Reliability, …

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A Model Of Competition Between Perpetual Software And Software As A Service, Zhiling Guo, Dan Ma Mar 2018

A Model Of Competition Between Perpetual Software And Software As A Service, Zhiling Guo, Dan Ma

Research Collection School Of Computing and Information Systems

Duplicate record, see https://ink.library.smu.edu.sg/sis_research/3744/. Software as a service (SaaS) has grown to be a significant segment of many software product markets. SaaS vendors, which charge customers based on use and continuously improve the quality of their products, have put competitive pressure on traditional perpetual software vendors, which charge a licensing fee and periodically upgrade the quality of their software. We develop an analytical model to study the competitive pricing strategies of an incumbent perpetual software vendor in the presence of a SaaS competitor. We find that, depending on both the SaaS quality improvement rate and the network effect, the …

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A Model Of Competition Between Perpetual Software And Software As A Service, Zhiling Guo, Dan Ma Mar 2018

A Model Of Competition Between Perpetual Software And Software As A Service, Zhiling Guo, Dan Ma

Research Collection School Of Computing and Information Systems

Software as a service (SaaS) has grown to be a significant segment of many software product markets. SaaS vendors, which charge customers based on use and continuously improve the quality of their products, have put competitive pressure on traditional perpetual software vendors, which charge a licensing fee and periodically upgrade the quality of their software. We develop an analytical model to study the competitive pricing strategies of an incumbent perpetual software vendor in the presence of a SaaS competitor. We find that, depending on both the SaaS quality improvement rate and the network effect, the perpetual software vendor adopts one …

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Do Your Friends Make You Buy This Brand?: Modeling Social Recommendation With Topics And Brands, Minh Duc Luu, Ee Peng Lim Mar 2018

Do Your Friends Make You Buy This Brand?: Modeling Social Recommendation With Topics And Brands, Minh Duc Luu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Consumer behavior and marketing research have shown that brand has significant influence on product reviews and product purchase decisions. However, there is very little work on incorporating brand related factors into product recommender systems. Meanwhile, the similarity in brand preference between a user and other socially connected users also affects her adoption decisions. To integrate seamlessly the individual and social brand related factors into the recommendation process, we propose a novel model called Social Brand–Item–Topic (SocBIT). As the original SocBIT model does not enforce non-negativity, which poses some difficulty in result interpretation, we also propose a non-negative version, called SocBIT(Formula …

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Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen Mar 2018

Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen

Research Collection School Of Computing and Information Systems

We present Automatic Persona Generation (APG), a methodology and system for quantitative persona generation using large amounts of online social media data. The system is operational, beta deployed with several client organizations in multiple industry verticals and ranging from small-to-medium sized enterprises to large multi-national corporations. Using a robust web framework and stable back-end database, APG is currently processing tens of millions of user interactions with thousands of online digital products on multiple social media platforms, such as Facebook and YouTube. APG identifies both distinct and impactful user segments and then creates persona descriptions by automatically adding pertinent features, such …

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An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang Mar 2018

An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Machine comprehension is concerned with teaching machines to answer reading comprehension questions. In this paper we adopt an LSTM-based model we designed earlier for textual entailment and propose two new models for cloze-style machine comprehension. In our first model, we treat the document as a premise and the question as a hypothesis, and use an LSTM with attention mechanisms to match the question with the document. This LSTM remembers the best answer token found in the document while processing the question. Furthermore, we observe some special properties of machine comprehension and propose a two-layer LSTM model. In this model, we …

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Mining Sandboxes: Are We There Yet?, Lingfeng Bao, Tien Duy B. Le, David Lo Mar 2018

Mining Sandboxes: Are We There Yet?, Lingfeng Bao, Tien Duy B. Le, David Lo

Research Collection School Of Computing and Information Systems

The popularity of Android platform on mobile devices has attracted much attention from many developers and researchers, as well as malware writers. Recently, Jamrozik et al. proposed a technique to secure Android applications referred to as mining sandboxes. They used an automated test case generation technique to explore the behavior of the app under test and then extracted a set of sensitive APIs that were called. Based on the extracted sensitive APIs, they built a sandbox that can block access to APIs not used during testing. However, they only evaluated the proposed technique with benign apps but not investigated whether …

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Urlnet: Learning A Url Representation With Deep Learning For Malicious Url Detection, Hung Le, Hong Quang Pham, Doyen Sahoo, Steven C. H. Hoi Mar 2018

Urlnet: Learning A Url Representation With Deep Learning For Malicious Url Detection, Hung Le, Hong Quang Pham, Doyen Sahoo, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is imperative to detect them in a timely manner. Traditionally, this is done through the usage of blacklists, which cannot be exhaustive, and cannot detect newly generated malicious URLs. To address this, recent years have witnessed several efforts to perform Malicious URL Detection using Machine Learning. The most popular and scalable approaches use lexical properties of the URL string by extracting Bag-of-words like features, followed by applying machine learning models such as SVMs. There are also other features designed by experts to improve the prediction performance of the …

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Visualizing Research Impact Through Citation Data, Yong Wang, Conglei Shi, Liangyue Li, Hanghang Tong, Huamin Qu Mar 2018

Visualizing Research Impact Through Citation Data, Yong Wang, Conglei Shi, Liangyue Li, Hanghang Tong, Huamin Qu

Research Collection School Of Computing and Information Systems

Research impact plays a critical role in evaluating the research quality and influence of a scholar, a journal, or a conference. Many researchers have attempted to quantify research impact by introducing different types of metrics based on citation data, such as h-index, citation count, and impact factor. These metrics are widely used in the academic community. However, quantitative metrics are highly aggregated in most cases and sometimes biased, which probably results in the loss of impact details that are important for comprehensively understanding research impact. For example, which research area does a researcher have great research impact on? How does …

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Attributed Social Network Embedding, Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua Mar 2018

Attributed Social Network Embedding, Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We …

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Building Trust In Artificial Intelligence, Machine Learning, And Robotics, Keng Siau, Weiyu Wang Mar 2018

Building Trust In Artificial Intelligence, Machine Learning, And Robotics, Keng Siau, Weiyu Wang

Research Collection School Of Computing and Information Systems

In this article, we look at trust in artificial intelligence, machine learning (ML), and robotics. We first review the concept of trust in AI and examine how trust in AI may be different from trust in other technologies. We then discuss the differences between interpersonal trust and trust in technology and suggest factors that are crucial in building initial trust and developing continuous trust in artificial intelligence.

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Identifying Self-Admitted Technical Debt In Open Source Projects Using Text Mining, Qiao Huang, Emad Shihab, Xin Xia, David Lo, Shanping Li Feb 2018

Identifying Self-Admitted Technical Debt In Open Source Projects Using Text Mining, Qiao Huang, Emad Shihab, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Technical debt is a metaphor to describe the situation in which long-term code quality is traded for short-term goals in software projects. Recently, the concept of self-admitted technical debt (SATD) was proposed, which considers debt that is intentionally introduced, e.g., in the form of quick or temporary fixes. Prior work on SATD has shown that source code comments can be used to successfully detect SATD, however, most current state-of-the-art classification approaches of SATD rely on manual inspection of the source code comments. In this paper, we proposed an automated approach to detect SATD in source code comments using text mining. …

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Attribute-Based Cloud Storage With Secure Provenance Over Encrypted Data, Hui Cui, Robert H. Deng, Yingjiu Li Feb 2018

Attribute-Based Cloud Storage With Secure Provenance Over Encrypted Data, Hui Cui, Robert H. Deng, Yingjiu Li

Research Collection School Of Computing and Information Systems

To securely and conveniently enjoy the benefits of cloud storage, it is desirable to design a cloud data storage system which protects data privacy from storage servers through encryption, allows fine-grained access control such that data providers can expressively specify who are eligible to access the encrypted data, enables dynamic user management such that the total number of data users is unbounded and user revocation can be carried out conveniently, supports data provider anonymity and traceability such that a data provider’s identity is not disclosed to data users in normal circumstances but can be traced by a trusted authority if …

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Upping The Game Of Taxi Driving In The Age Of Uber, Shashi Shekhar Jha, Shih-Fen Cheng, Meghna Lowalekar, Wai Hin Wong, Rajendram Rishikeshan Rajendram, Trong Khiem Tran, Pradeep Varakantham, Nghia Truong Trong, Firmansyah Abd Rahman Feb 2018

Upping The Game Of Taxi Driving In The Age Of Uber, Shashi Shekhar Jha, Shih-Fen Cheng, Meghna Lowalekar, Wai Hin Wong, Rajendram Rishikeshan Rajendram, Trong Khiem Tran, Pradeep Varakantham, Nghia Truong Trong, Firmansyah Abd Rahman

Research Collection School Of Computing and Information Systems

In most cities, taxis play an important role in providing point-to-point transportation service. If the taxi service is reliable, responsive, and cost-effective, past studies show that taxi-like services can be a viable choice in replacing a significant amount of private cars. However, making taxi services efficient is extremely challenging, mainly due to the fact that taxi drivers are self-interested and they operate with only local information. Although past research has demonstrated how recommendation systems could potentially help taxi drivers in improving their performance, most of these efforts are not feasible in practice. This is mostly due to the lack of …

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Modelling Domain Relationships For Transfer Learning On Retrieval-Based Question Answering Systems In E-Commerce, Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen Feb 2018

Modelling Domain Relationships For Transfer Learning On Retrieval-Based Question Answering Systems In E-Commerce, Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen

Research Collection School Of Computing and Information Systems

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning …

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Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi Feb 2018

Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the …

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Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein Feb 2018

Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers.The model is grounded in two multi-robot meeting and box pushing domains that …

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