This paper considers the restoration of multiple interdependent infrastructures after a man-made or natural disaster. Modern infrastructures feature complex cyclic interdependencies and require a holistic restoration process. This paper presents the first scalable approach for the last-mile restoration of the joint electrical power and gas infrastructures. It builds on an earlier three-stage decomposition for restoring the power network that decouples the restoration ordering and the routing aspects. The key contributions of the paper are (1) mixed-integer programming models for finding a minimal restoration set and a restoration ordering and (2) a randomized adaptive decomposition to obtain high-quality solutions within the required time constraints. The approach is validated on a large selection of benchmarks based on the United States infrastructures and state-of-the-art weather and fragility simulation tools. The results show significant improvements over current field practices.

This paper presents a dynamic role assignment and formation positioning system used by the 2011 RoboCup 3D simulation league champion UT Austin Villa. This positioning system was a key component in allowing the team to win all 24 games it played at the competition during which the team scored 136 goals and conceded none. The positioning system was designed to allow for decentralized coordination among physically realistic simulated humanoid soccer playing robots in the partially observable, non-deterministic, noisy, dynamic, and limited communication setting of the RoboCup 3D simulation league simulator. Although the positioning system is discussed in the context of the RoboCup 3D simulation environment, it is not domain specific and can readily be employed in other RoboCup leagues as it generalizes well to many realistic and real-world multiagent systems.

The ESP Game tends to generate "low effort" or "surface semantics" tags. This paper presents two variations of the ESP Games called "squaring" and "scripting" that trim the ESP Game to collect "deep semantics" tags. The approaches do not require players to get used to, and for the GWAP operators to deploy, new games. First experiments point to the efficiency of squaring and scripting the ESP Game at collecting "deep semantic" tags.

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar’s original symbolic processing, which improves Soar abilities for control of robots. These extensions include mental imagery, episodic and semantic memory, reinforcement learning, and continuous model learning. This paper presents research in mobile robotics, relational and continuous model learning, and learning by situated, interactive instruction.

It is well established that in certain domains, noisy inputs can be reliablycombined to obtain a better answer than any individual.It is now possible to consider the crowdsourcing of physical actions,commonly used for creative expressions such as drawing, shading, and singing.We provide algorithms for converting low-quality inputobtained from the physical actions of a crowd into high-quality output.The inputs take the form of line drawings, shaded images, and songs.We investigate single-individual crowds (multiple inputs from a single human)and multiple-individual crowds.

Cooperative multi-agent path planning around a common location has many applications, and has received significant at- tention from the research community. Our research is motivated by the need for groups of autonomous vehicles or mobile robots to collaboratively plan efficient paths around shared navigational coordinates (waypoints) in a distributed and decentralized manner. Our ongoing research is focused on creating a distributed solution to Dresner and Stone’s Autonomous Intersection Management problem. In the future we plan to relax the constraints of this problem, and allow more flexibility in the angles of approach and departure from a single waypoint, and also plan to consider efficient group plans for multi-waypoint routes. In this paper we briefly introduce intersection management, present preliminary results for an unstructured peer-to-peer approach to the problem, and discuss future research directions.

Interest in online education is surging, as dramatized bythe success of Khan Academy and recent Stanford online courses, but the technology for online education isin its infancy. Crowdsourcing mechanisms will likelybe essential in order to reach the full potential of thismedium. This paper sketches some of the challengesand directions we hope HCOMP researchers will address.

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices.

In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of such fines depends on the unpredictability and effectiveness of the patrols. In this paper, we present TRUSTS, an application for scheduling randomized patrols for fare inspection in transit systems. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to be executed. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff’s department has begun trials of TRUSTS.

We investigate machine learning methods for diagnostic screening of heart disease. Coronary heart disease is the leading cause of death in the US, causing more deaths than all types of cancers combined. Early diagnosis of heart disease in women is harder than it is in men and typically requires the administration of several clinical tests on the patient. Most risk stratification methods aggregate the results of such tests, including the risky, invasive procedures that cannot be administered on all patients. In this paper, our goal is to identify patients who are under high-risk of having heart disease and related adverse events, using a minimal number of diagnostic tests, especially less invasive ones. The low frequency of patients with severe heart disease in the dataset is challenging for most conventional machine learning methods. To overcome this problem, we develop and apply a cost-sensitive k nearest neighbor algorithm. Our contributions are two fold: First, we compare the predictive value of several diagnostic procedures for heart disease, including electrocardiography, angiography, radionuclide perfusion and conclude that in womens heart disease, certain combinations of non-invasive techniques are more predictive than some of the widely used invasive procedures. Then, we evaluate held out data and achieve an AUROC over 0.70, signifying valuable clinical utility, using only the least costly and least invasive tests.