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Events for May
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PhD Defense - Matthew Brown
Fri, May 01, 2015 @ 02:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Matthew Brown
Committee:
Milind Tambe (Chair)
Jonathan Gratch
Ewa Deelman
Richard John
Dale Kiefer
Title: Balancing Tradeoffs in Security Games: Handling Defenders and Adversaries with Multiple Objectives
Abstract:
Stackelberg security games (SSG) have received a significant amount of attention in the literature for modeling the strategic interactions between a defender and an adversary, in which the defender has a limited amount of security resources to protect a set of targets from a potential attack by the adversary. SSGs are at the heart of several significant decision-support applications deployed in real world security domains. All of these applications rely on standard assumptions made in SSGs, including that the defender and the adversary each have a single objective which is to maximize their expected utility. Given the successes and real world impact of previous SSG research, there is a natural desire to push towards increasingly complex security domains, leading to a point where considering only a single objective is no longer appropriate.
My thesis focuses on incorporating multiple objectives into SSGs. With multiple conflicting objectives for either the defender or adversary, there is no one solution which maximizes all objectives simultaneously and tradeoffs between the objectives must be made. Thus, my thesis provides two main contributions by addressing the research challenges raised by considering SSGs with (1) multiple defender objectives and (2) multiple adversary objectives. These contributions consist of approaches for modeling, calculating, and analyzing the tradeoffs between objectives in a variety of different settings. First, I consider multiple defender objectives resulting from diverse adversary threats where protecting against each type of threat is treated as a separate objective for the defender. Second, I investigate the defender's need to balance between the exploitation of collected data and the exploration of alternative strategies in patrolling domains. Third, I explore the necessary tradeoff between the efficacy and the efficiency of the defender's strategy in screening domains. Forth, I examine multiple adversary objectives for heterogeneous populations of boundedly rational adversaries that no longer strictly maximize expected utility.
The contributions of my thesis provide the novel game models and algorithmic techniques required to incorporate multiple objectives into SSGs. My research advances the state of the art in SSGs and opens up the model to new types of security domains that could not have been handled previously. As a result, I developed two applications for real world security domains that either have been or will be tested and evaluated in the field.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Aaron St. Clair
Tue, May 12, 2015 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Date: Tuesday, May 12, 2015
Time: 12pm
Location: RTH 406
PhD Candidate: Aaron St. Clair
Committee:
Maja MatariÄ (Chair)
Gaurav Sukhatme
Nora Ayanian
Aaron Hagedorn
Title: Coordinating Communication in Human-Robot Task Collaborations
Abstract:
Robots have become increasingly capable of performing a variety of tasks in real-world dynamic environments, including those involving people. Beyond competently performing the tasks required of them, service robots should also be able to coordinate their actions with those of the people around them in order to minimize conflicts, provide feedback, and build rapport with human teammates in both work environments (e.g., manufacturing) and home settings. Humans coordinate their actions in various task settings through structured social interaction aimed at representational alignment and intentional feedback. In order for robots to coordinate their actions using similar modalities, they must be capable of contextualizing the actions of human partners and producing relevant natural communicative behaviors as the task progresses. This dissertation is motivated by the high-level goal of producing effective social feedback during task performance, and alleviating the burden of coordinating the team's joint activity by allowing human users to interact with robots through natural social modalities as partners rather than as operators.
This dissertation develops an approach for constructing and generalizing models of role-based coordinating communication during physically-decoupled human-robot task scenarios, specifically pairwise collaborations in which a person and a robot work together to achieve a shared goal. The approach is validated in different task contexts with different user populations using objective and subjective measures of task performance and user preferences. To support role-allocative communication observed in our pilot experiments with two-person teams, the human-robot collaboration problem is formulated as a Markov decision process in which roles are represented by a set of policies capturing different action selection preferences and accounting for unequal capabilities between human and robot collaborators. A probabilistic method is used to track the user's activity over time and to recognize the role assumed by the user, communication is then planned given the expected policy of the user, the policy of the robot, and the current task state. The communication generated by the robot consists of three types of speech actions and associated co-verbal behavior: 1) self-narration of the robot's activities, 2) role allocation suggestions for the user, and 3) empathetic displays when positive and negative state changes occur.
The approach was validated initially on a dynamic augmented reality herding task with a population of convenience users using objective metrics (idle time, distance traveled) as well as subjective evaluations (user preference, perceived intelligence of the robot), where a higher utilization of the robot and more equitable path distance was observed in comparison to a non-communicating robot. The generalizability of the approach to a different task setting and user population was also evaluated on a cooking task with an elderly user population. The contributions of this dissertation lie in the development of an approach for modeling human-robot task performance for the planning and production of effective robot verbal feedback.Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - Xun Fan
Thu, May 28, 2015 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense - Xun Fan
May 28, 2015
10am-12pm
SAL 213
Committee:
John Heidemann (chair)
Ramesh Govindan
Ethan Katz-Bassett
Konstantinos Psounis
ENABLING EFFICIENT SERVICE ENUMERATION THROUGH SMART
SELECTION OF MEASUREMENTS
The Internet is becoming more and more important in our daily lives. Both the government and industry invest in the growth of the Internet, bringing more users to the world of networks. As the Internet grows, researchers and operators need to track and understand the behavior of global Internet services to achieve smooth operation. Active measurements are often used to study behavior of large Internet service, and efficient service enumeration is required. For example, studies of Internet topology may need active probing to all visible network prefixes; monitoring large replicated service requires periodical enumeration of all service replicas. To achieve efficient service enumeration, it is important to select probing sources and destinations wisely. However, there are challenges for making smart selection of probing sources and destinations. Prior methods to select probing destinations are either inefficient or hard to maintain. Enumerating replicas of large Internet services often requires many widely distributed probing sources. Current measurement platforms don't have enough probing sources to approach complete
enumeration of large services.
This dissertation makes the thesis statement that smart selection of probing sources and destinations enables efficient enumeration of global Internet services to track
and understand their behavior. We present three studies to demonstrate this thesis statement. First, we propose new automated approach to generate a list of destination
IP addresses that enables efficient enumeration of Internet edge links. Second, we show that using large number of widely distributed open resolvers enables efficient enumeration of anycast nodes which helps study abnormal behavior of anycast DNS services. In our last study, we efficiently enumerate Front-End (FE) Clusters of Content Delivery Networks (CDNs) and use the efficient enumeration to track and understand the dynamics of user-to-FE Cluster mapping of large CDNs. We achieve the efficient enumeration of CDN FE Clusters by selecting probing sources from a large set of open resolvers.
Our selected probing sources have smaller number of open resolvers but provide same coverage on CDN FE Cluster as the larger set.
In addition to our direct results, our work has also been used by several published studies to track and understand the behavior of Internet and large network services.
These studies not only support our thesis as additional examples but also suggest this thesis can further benefit many other studies that need efficient service enumeration to
track and understand behavior of global Internet services.
Location: 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
