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.

In this paper, we propose an approach that automatically extract users’ activities in sentences retrieved from Twitter. We then design a timeline action networkbased on Web Ontology Language (OWL). By using the proposed activity extraction approach, we can automatically collect data for the action network. Finally, we propose a novel action-based collaborative filtering, which predicts missing activity data, in order to complement this timeline network. Moreover, with a combination of collaborative filtering and natural language processing (NLP), our method can deal with minority actions such as successful actions. Based on evaluation of tweets which related to the massive Tohoku earthquake,we indicated that our timeline action network can provide useful action patterns in real-time. Not only earthquake disaster, our research can also be applied to other disasters and business models, such as typhoon,travel, marketing, etc.

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.

Research over the past years has shown that machine translation results can be greatly enhanced with the help of mono- or bilingual human contributors, e.g. by asking hu- mans to proofread or correct outputs of machine translation systems. However, it remains difficult to determine the quality of individual revisions. This paper proposes a meth- od to determine the quality of individual contributions by analyzing task-independent data. Examples of such data are completion time, number of keystrokes, etc. An initial evaluation showed promising F-measure values larger than 0.8 for support vector machine and decision tree based classifications of a combined test set of Vietnamese and German translations.

General representation, abstraction and exchange definitions are crucial for dynamically configurable context recognition. However, to evaluate potential definitions, suitable standard datasets are needed. This paper presents our effort to create and maintain large scale, multimodal standard datasets for context recognition research. We ourselves used these datasets in previous research to deal with placement effects and presented low-level sensor abstractions in motion based on-body sensing. Researchers, conducting novel data collections, can rely on the toolchain and the the low-level sensor abstractions summarized in this paper. Additionally, they can draw from our experiences developing and conducting context recognition experiments. Our toolchain is already a valuable rapid prototyping tool. Still, we plan to extend it to crowd-based sensing, enabling the general public to gather context data, learn more about their lives and contribute to context recognition research. Applying higher level context reasoning on the gathered context data is a obvious extension to our work.

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.

Adaptive Cruise Control (ACC) is a technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Individual drivers have different driving styles and preferences. Current systems do not distinguish among the users. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can save on the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While accepted packages such as Weka were successful in learning drivers' behavior, we found that improved learning models could be developed by adding information on drivers' demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.

When designing a complex system, adhering to a design methodology is essential to ensure design quality and to shorten the design phase. Until recently, enforcing this could be done only partially or manually. This paper demonstrates how constraint programming technology can enable automation of the design methodology support when the design artifacts reside in a central repository. At any phase of the design, the proposed constraint programming application can indicate whether the design process data complies with the methodology and point out any violations that may exist. Moreover, the application can provide recommendations regarding the design process. The application was successfully used to check the methodology conformance of an industrial example and produced the desired outputs within reasonable times.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford’s assembly plants in non-English speaking countries. This project has continued to evolve with the addition of new languages and improvements to the translation process. However, we discovered that there was a large demand for automated language translation across all of Ford Motor Company and we decided to expand the scope of our project to address these requirements. This paper will describe our efforts to meet all of Ford’s internal translation requirements with AI and MT technology and focus on the challenges and lessons that we learned from applying advanced technology across an entire corporation.