Human-robot interaction poses tight timing requirements on visual as well as natural language processing in order to allow for natural human-robot interaction. In particular, humans expect robots to incrementally resolve spoken references to visually perceivable objects as the referents are verbally described. In this paper, we present an integrated robotic architecture with novel incremental vision and natural language processing and demonstrate that incrementally refining attentional focus using linguistic constraints achieves significantly better performance of the vision system compared to non-incremental visual processing.

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.

This paper documents extending the ELEXIR (Engine for LEXicalized Intent Recognition) system (Geib 2009; Geib 2011) with a world model. This is a significant increase in the expressiveness of the plan recognition system and allows a number of additions to the algorithm, most significantly conditioning probabilities for recognized plans on the state of the world during execution. Since, ELEXIR falls in the family of gramatical methods for plan recognition in viewing the problem of plan recognition as that of parsing, this paper will also briefly discuss how this extension relates to state of the art proposals in the natural language community regarding probabilistic parsing.

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.

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.

We present a novel approach to bidirectional breadth-first IDA* (BFIDA*) and demonstrate its effectiveness in the domain of peg solitaire, a simple puzzle. Our approach improves upon unidirectional BFIDA* by usually avoiding the last iteration of search entirely, greatly speeding up search. In addition, we provide a number of improvements specific to peg solitaire. We have improved duplicate-detection in the context of BFIDA*. We have strengthened the heuristic used in the previous state-of-the-art solver. Finally, we use bidirectional search frontiers to provide a stronger technique for pruning unsolvable states. The combination of these approaches allows us to improve over the previous state-of-the-art, often by a two-orders-of-magnitude reduction in search time.

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.