Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (3441)
- Software Engineering (2143)
- Artificial Intelligence and Robotics (1665)
- Information Security (1057)
- Numerical Analysis and Scientific Computing (1024)
-
- Graphics and Human Computer Interfaces (921)
- Engineering (858)
- Social and Behavioral Sciences (661)
- Business (625)
- Theory and Algorithms (493)
- Computer Engineering (431)
- Operations Research, Systems Engineering and Industrial Engineering (400)
- Programming Languages and Compilers (379)
- OS and Networks (323)
- Communication (297)
- Social Media (240)
- Public Affairs, Public Policy and Public Administration (207)
- Transportation (185)
- Medicine and Health Sciences (178)
- Education (164)
- Management Information Systems (164)
- Data Storage Systems (160)
- E-Commerce (146)
- Health Information Technology (107)
- International and Area Studies (107)
- Asian Studies (106)
- Technology and Innovation (100)
- Digital Communications and Networking (96)
- Keyword
-
- Deep learning (123)
- Machine learning (121)
- Social media (74)
- Artificial intelligence (70)
- Reinforcement learning (69)
-
- Data mining (64)
- Privacy (61)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (54)
- Optimization (53)
- Security (53)
- Visualization (51)
- Software engineering (49)
- Training (49)
- Neural networks (48)
- Online learning (48)
- Task analysis (48)
- Anomaly detection (47)
- Singapore (47)
- Twitter (46)
- Feature extraction (45)
- Blockchain (44)
- Collaboration (44)
- Large Language Models (43)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Machine Learning (38)
- Publication Year
- File Type
Articles 5191 - 5220 of 8481
Full-Text Articles in Computer Sciences
Ranking Of High-Value Social Audiences On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Ranking Of High-Value Social Audiences On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
Even though social media offers plenty of business opportunities, for a company to identify the right audience from the massive amount of social media data is highly challenging given finite resources and marketing budgets. In this paper, we present a ranking mechanism that is capable of identifying the top-k social audience members on Twitter based on an index. Data from three different Twitter business account owners were used in our experiments to validate this ranking mechanism. The results show that the index developed using a combination of semi-supervised and supervised learning methods is indeed generic enough to retrieve relevant audience …
One Size Does Not Fit All: A Game-Theoretic Approach For Dynamically And Effectively Screening For Threats, Matthew Brown, Arunesh Sinha, Aaron Schlenker, Milind Tambe
One Size Does Not Fit All: A Game-Theoretic Approach For Dynamically And Effectively Screening For Threats, Matthew Brown, Arunesh Sinha, Aaron Schlenker, Milind Tambe
Research Collection School Of Computing and Information Systems
An effective way of preventing attacks in secure areas is to screen for threats (people, objects) before entry, e.g., screening of airport passengers. However, screening every entity at the same level may be both ineffective and undesirable. The challenge then is to find a dynamic approach for randomized screening, allowing for more effective use of limited screening resources, leading to improved security. We address this challenge with the following contributions: (1) a threat screening game (TSG) model for general screening domains; (2) an NP-hardness proof for computing the optimal strategy of TSGs; (3) a scheme for decomposing TSGs into subgames …
Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu
Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
Distance metric learning (DML) is an important technique to improve similarity search in content-based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single-modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single-modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high-dimensional feature space can be extremely time-consuming using the …
Exploring Heterogeneous Features For Query-Focused Summarization Of Categorized Community Answers, Wei Wei, Zhaoyan Ming, Liqiang Nie, Guohui Li, Jianjun Li, Feida Zhu, Tianfeng Shang, Changyin Luo
Exploring Heterogeneous Features For Query-Focused Summarization Of Categorized Community Answers, Wei Wei, Zhaoyan Ming, Liqiang Nie, Guohui Li, Jianjun Li, Feida Zhu, Tianfeng Shang, Changyin Luo
Research Collection School Of Computing and Information Systems
Community-based question answering (cQA) is a popular type of online knowledge-sharing web service where users ask questions and obtain answers contributed by others. To enhance knowledge sharing, cQA also provides users with a retrieval function to access the historical question-answer pairs (QAs). However, it is still ineffective in that the retrieval result is typically a ranking list of potentially relevant QAs, rather than a succinct and informative answer. To alleviate the problem, this paper proposes a three-level scheme, which aims to generate a query-focused summary-style answer in terms of two factors, i.e., novelty and redundancy. Specifically, we first retrieve a …
Ambient And Smartphone Sensor Assisted Adl Recognition In Multi-Inhabitant Smart Environments, Nirmalya Roy, Archan Misra, Diane Cook
Ambient And Smartphone Sensor Assisted Adl Recognition In Multi-Inhabitant Smart Environments, Nirmalya Roy, Archan Misra, Diane Cook
Research Collection School Of Computing and Information Systems
Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users’ spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user …
Efficient Collective Spatial Keyword Query Processing On Road Networks, Yunjun Gao, Jingwen Zhao, Baihua Zheng, Gang Chen
Efficient Collective Spatial Keyword Query Processing On Road Networks, Yunjun Gao, Jingwen Zhao, Baihua Zheng, Gang Chen
Research Collection School Of Computing and Information Systems
The collective spatial keyword query (CSKQ), an important variant of spatial keyword queries, aims to find a set of the objects that collectively cover users' queried keywords, and those objects are close to the query location and have small inter-object distances. Existing works only focus on the CSKQ problem in the Euclidean space, although we observe that, in many real-life applications, the closeness of two spatial objects is measured by their road network distance. Thus, existing methods cannot solve the problem of network-based CSKQ efficiently. In this paper, we study the problem of collective spatial keyword query processing on road …
Negative Factor: Improving Regular-Expression Matching In Strings, Xiaochun Yang, Tao Qiu, Bin Wang, Baihua Zheng, Yaoshu Wang, Chen Li
Negative Factor: Improving Regular-Expression Matching In Strings, Xiaochun Yang, Tao Qiu, Bin Wang, Baihua Zheng, Yaoshu Wang, Chen Li
Research Collection School Of Computing and Information Systems
The problem of finding matches of a regular expression (RE) on a string exists in many applications such as text editing, biosequence search, and shell commands. Existing techniques first identify candidates using substrings in the RE, then verify each of them using an automaton. These techniques become inefficient when there are many candidate occurrences that need to be verified. In this paper we propose a novel technique that prunes false negatives by utilizing negative factors, which are substrings that cannot appear in an answer. A main advantage of the technique is that it can be integrated with many existing algorithms …
Mobile App Tagging, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao
Mobile App Tagging, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. …
Ict-Travel: Mobile Public Transport Companion For The Visually Impaired, Linting Cui, Kenny Ngo, Benjamin Kok Siew Gan
Ict-Travel: Mobile Public Transport Companion For The Visually Impaired, Linting Cui, Kenny Ngo, Benjamin Kok Siew Gan
Research Collection School Of Computing and Information Systems
The smartphone application widespread adoption has brought about many conveniences to the general population. Unfortunately, like most technology adoption, the focus lacks behind for people with disabilities. Yet, the potential for IT to personalize the mobile application for these groups is high. In our capstone project at Singapore Management University, we developed an iOS application for the visually impaired to use the public transport in Singapore. Beyond meeting the initial requirements, we tested with the visually impaired in order to empathize and cater to their specific needs. This software engineering project is a lesson in iterative software development with changing …
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Research Collection School Of Computing and Information Systems
We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …
Online Cross-Modal Hashing For Web Image Retrieval, Liang Xie, Jialie Shen, Lei Zhu
Online Cross-Modal Hashing For Web Image Retrieval, Liang Xie, Jialie Shen, Lei Zhu
Research Collection School Of Computing and Information Systems
Cross-modal hashing (CMH) is an efficient technique for the fast retrieval of web image data, and it has gained a lot of attentions recently. However, traditional CMH methods usually apply batch learning for generating hash functions and codes. They are inefficient for the retrieval of web images which usually have streaming fashion. Online learning can be exploited for CMH. But existing online hashing methods still cannot solve two essential problems: Efficient updating of hash codes and analysis of cross-modal correlation. In this paper, we propose Online Cross-modal Hashing (OCMH) which can effectively address the above two problems by learning the …
Solving Risk-Sensitive Pomdps With And Without Cost Observations, Ping Hou, William Yeoh, Pradeep Varakantham
Solving Risk-Sensitive Pomdps With And Without Cost Observations, Ping Hou, William Yeoh, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in Risk-Sensitive POMDPs (RS-POMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new search-based algorithm to solve RS-POMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated …
Robust Decision Making For Stochastic Network Design, Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
Robust Decision Making For Stochastic Network Design, Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
Research Collection School Of Computing and Information Systems
We address the problem of robust decision making for stochastic network design. Our work is motivated by spatial conservation planning where the goal is to take management decisions within a fixed budget to maximize the expected spread of a population of species over a network of land parcels. Most previous work for this problem assumes that accurate estimates of different network parameters (edge activation probabilities, habitat suitability scores) are available, which is an unrealistic assumption. To address this shortcoming, we assume that network parameters are only partially known, specified via interval bounds. We then develop a decision making approach that …
Nlu Framework For Voice Enabling Non-Native Applications On Smart Devices, Soujanya Lanka, Deepika Panthania, Pooja Kushalappa, Pradeep Varakantham
Nlu Framework For Voice Enabling Non-Native Applications On Smart Devices, Soujanya Lanka, Deepika Panthania, Pooja Kushalappa, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Voice is a critical user interface on smart devices (wearables, phones, speakers, televisions) to access applications (or services) available on them. Unfortunately, only a few native applications (provided by the OS developer) are typically voice enabled in devices of today. Since, the utility of a smart device is determined more by the strength of external applications developed for the device, voice enabling non-native applications in a scalable, seamless manner within the device is a critical use case and is the focus of our work. We have developed a Natural Language Understanding (NLU) framework that uses templates supported by the application …
Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Spatio-temporal matching of services to customers online is a problem that arises on a large scale in many domains associated with shared transportation (ex: taxis, ride sharing, super shuttles, etc.) and delivery services (ex: food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that matching of services to customers in one round has a direct impact on the matching of services to customers in the next round. For instance, in the case of taxis, in the second round taxis can only pick up customers closer to the drop off point of the customer from the first …
Multiagent Based Algorithmic Approach For Fast Response In Railway Disaster Handling, Poulami Dalapati, Arambam James Singh, Animesh Dutta
Multiagent Based Algorithmic Approach For Fast Response In Railway Disaster Handling, Poulami Dalapati, Arambam James Singh, Animesh Dutta
Research Collection School Of Computing and Information Systems
Disaster management in railway network is an important issue. It requires to minimize negative impact and also fast, efficient recovery from the disturbances. The main challenge here is that, the effect of inconvenience spreads out very fast in time and space. It takes noticeable amount of time to get back everything in the previous situation. This paper proposes a multi agent based algorithmic approach for disaster handling in Railway Network. This takes care of fast response to get total number of affected trains in a fast and efficient manner. We propose few algorithms to handle this situation and simulate it …
Online Advertising, Retail Platform Openness, And Long Tail Sellers, Jianqing Chen, Zhiling Guo
Online Advertising, Retail Platform Openness, And Long Tail Sellers, Jianqing Chen, Zhiling Guo
Research Collection School Of Computing and Information Systems
No abstract provided.
Copyright Law And The Supply Of Creative Work: Evidence From The Movies, Ivan Paak Liang Png, Qiu-Hong Wang
Copyright Law And The Supply Of Creative Work: Evidence From The Movies, Ivan Paak Liang Png, Qiu-Hong Wang
Research Collection School Of Computing and Information Systems
There is almost no empirical evidence on the extent to whichcopyright law works in the sense of increasing the production of creative work.Here, we study the impact of two major changes in copyright law – the extensionof copyright term and the European Rental Directive – on the production ofmovies. In a panel of 23 OECD countries, among which 19 extendedcopyright term at various times between 1991–2005, we found no statisticallyrobust evidence that copyright term extension was associated with higher movie production.In a panel of 17 European countries between 1991–2005, wefound no statistically robust evidence that compliance with the RentalDirective was …
Accurate Online Video Tagging Via Probabilistic Hybrid Modeling, Jialie Shen, Meng Wang, Tat-Seng Chua
Accurate Online Video Tagging Via Probabilistic Hybrid Modeling, Jialie Shen, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Accurate video tagging has been becoming increasingly crucial for online video management and search. This article documents a novel framework called comprehensive video tagger (CVTagger) to facilitate accurate tag-based video annotation. The system applies both multimodal and temporal properties combined with a novel classification framework with hierarchical structure based on multilayer concept model and regression analysis. The advanced architecture enables effective incorporation of both video concept dependency and temporal dynamics. Using a large-scale test collection containing 50,000 YouTube videos, a set of empirical studies have been carried out and experimental results demonstrate various advantages of CVTagger over the state-of-the-art techniques.
Multiagent-Based Route Guidance For Increasing The Chance Of Arrival On Time, Zhiguang Cao, Hongliang Guo, Jie Zhang, Ulrich Fastenrath
Multiagent-Based Route Guidance For Increasing The Chance Of Arrival On Time, Zhiguang Cao, Hongliang Guo, Jie Zhang, Ulrich Fastenrath
Research Collection School Of Computing and Information Systems
Transportation and mobility are central to sustainable urban development, where multiagent-based route guidance is widely applied. Traditional multiagent-based route guidance always seeks LET (least expected travel time) paths. However, drivers usually have specific expectations, i.e., tight or loose deadlines, which may not be all met by LET paths. We thus adopt and extend the probability tail model that aims to maximize the probability of reaching destinations before deadlines. Specifically, we propose a decentralized multiagent approach, where infrastructure agents locally collect intentions of concerned vehicle agents and formulate route guidance as a route assignment problem, to guarantee their arrival on time. …
Online Learning Of Arima For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun
Online Learning Of Arima For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun
Research Collection School Of Computing and Information Systems
Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to …
Ibed: Combining Ibea And De For Optimal Feature Selection In Software Product Line Engineering, Yinxing Xue, Jinghui Zhong, Tian Huat Tan, Yang Liu, Wentong Cai, Manman Chen, Jun Sun
Ibed: Combining Ibea And De For Optimal Feature Selection In Software Product Line Engineering, Yinxing Xue, Jinghui Zhong, Tian Huat Tan, Yang Liu, Wentong Cai, Manman Chen, Jun Sun
Research Collection School Of Computing and Information Systems
Software configuration, which aims to customize the software for different users (e.g., Linux kernel configuration), is an important and complicated task. In software product line engineering (SPLE), feature oriented domain analysis is adopted and feature model is used to guide the configuration of new product variants. In SPLE, product configuration is an optimal feature selection problem, which needs to find a set of features that have no conflicts and meanwhile achieve multiple design objectives (e.g., minimizing cost and maximizing the number of features). In previous studies, several multi-objective evolutionary algorithms (MOEAs) were used for the optimal feature selection problem and …
Formalizing And Verifying Stochastic System Architectures Using Monterey Phoenix, Songzheng Song, Jiexin Zhang, Yang Liu, Mikhail Auguston, Jun Sun, Jin Song Dong, Tieming Chen
Formalizing And Verifying Stochastic System Architectures Using Monterey Phoenix, Songzheng Song, Jiexin Zhang, Yang Liu, Mikhail Auguston, Jun Sun, Jin Song Dong, Tieming Chen
Research Collection School Of Computing and Information Systems
The analysis of software architecture plays an important role in understanding the system structures and facilitate proper implementation of user requirements. Despite its importance in the software engineering practice, the lack of formal description and verification support in this domain hinders the development of quality architectural models. To tackle this problem, in this work, we develop an approach for modeling and verifying software architectures specified using Monterey Phoenix (MP) architecture description language. MP is capable of modeling system and environment behaviors based on event traces, as well as supporting different architecture composition operations and views. First, we formalize the syntax …
Improved Egt-Based Robustness Analysis Of Negotiation Strategies In Multiagent Systems Via Model Checking, Songzheng Song, Jianye Hao, Yang Liu, Jun Sun, Ho-Fung Leung, Jie Zhang
Improved Egt-Based Robustness Analysis Of Negotiation Strategies In Multiagent Systems Via Model Checking, Songzheng Song, Jianye Hao, Yang Liu, Jun Sun, Ho-Fung Leung, Jie Zhang
Research Collection School Of Computing and Information Systems
Automated negotiations play an important role in various domains modeled as multiagent systems, where agents represent human users and adopt different negotiation strategies. Generally, given a multiagent system, a negotiation strategy should be robust in the sense that most agents in the system have the incentive to choose it rather than other strategies. Empirical game-theoretic (EGT) analysis is a game-theoretic analysis approach to investigate the robustness of different strategies based on a set of empirical results. In this study, we propose that model-checking techniques can be adopted to improve EGT analysis for negotiation strategies. The dynamics of strategy profiles can …
Experience Me! The Impact Of Content Sampling Strategies On The Marketing Of Digital Entertainment Goods, Ai Phuong Hoang, Robert J. Kauffman
Experience Me! The Impact Of Content Sampling Strategies On The Marketing Of Digital Entertainment Goods, Ai Phuong Hoang, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
Product sampling allows consumers to try out a small portion of a product for free. Uncertainty associated with consumption of information goods makes sampling useful for digital entertainment providers. Firms offer some programming for free to attract consumers to purchase a series of programs. We explore the effectiveness of content sampling for information goods using a dataset containing more than 17 million free previews and purchase observations on households from a digital entertainment firm that offers video-on-demand (VoD). Based on theories related to product sampling and information goods, we analyze the relationship between free previews and VoD purchases for series …
Information Source Detection Via Maximum A Posteriori Estimation, Biao Chang, Feida Zhu, Enhong Chen, Qi. Liu
Information Source Detection Via Maximum A Posteriori Estimation, Biao Chang, Feida Zhu, Enhong Chen, Qi. Liu
Research Collection School Of Computing and Information Systems
The problem of information source detection, whose goal is to identify the source of a piece of information from a diffusion process (e.g., computer virus, rumor, epidemic, and so on), has attracted ever-increasing attention from research community in recent years. Although various methods have been proposed, such as those based on centrality, spectral and belief propagation, the existing solutions still suffer from high time complexity and inadequate effectiveness. To this end, we revisit this problem in the paper and present a comprehensive study from the perspective of likelihood approximation. Different from many previous works, we consider both infected and uninfected …
Demo: Sound Localization Using Smartphone, Amit Sharma, Youngki Lee
Demo: Sound Localization Using Smartphone, Amit Sharma, Youngki Lee
Research Collection School Of Computing and Information Systems
Smartphones based sound direction estimation can be helpful in many situations. For example, a deaf person in a meeting room can look at the smartphone to find out which direction the speaker is in and then he can look in appropriate direction to read lips/gestures of the speaker. Many smartphones today come with two built-in microphones located at physically different positions. This difference in position can cause time difference of arrival (TDOA) of sound on both microphones. Value of TDOA for two microphones may vary depending on the location of sound source with respect to the smartphone. This time difference …
A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak
A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak
Research Collection School Of Computing and Information Systems
In 2015, Singaporean have experienced one of the worse air pollution crises in history. With datasets from a well-known photo sharing social network, we analyze how this haze affects Singaporean's daily life. We will share our preliminary results in this paper.
Towards A Science Of Security Games, Thanh Hong Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe
Towards A Science Of Security Games, Thanh Hong Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe
Research Collection School Of Computing and Information Systems
Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types.Computational game theory can help design such security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the …
An Extended Study On Addressing Defender Teamwork While Accounting For Uncertainty In Attacker Defender Games Using Iterative Dec-Mdps, Eric Shieh, Albert Xin Jiang, Amulya Yadav, Pradeep Varakantham, Milind Tambe
An Extended Study On Addressing Defender Teamwork While Accounting For Uncertainty In Attacker Defender Games Using Iterative Dec-Mdps, Eric Shieh, Albert Xin Jiang, Amulya Yadav, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
Multi-agent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multi-agent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. The problem that this paper seeks to solve is the coordination of decentralized defender agents in the presence of uncertainty while securing targets against an observing adversary. To address this problem, we offer the following novel contributions in this paper: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column …