We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. The equivalence holds when energy distances are computed with semimetrics of negative type, in which case a kernel may be defined such that the RKHS distance between distributions corresponds exactly to the energy distance. We determine the class of probability distributions for which kernels induced by semimetrics are characteristic (that is, for which embeddings of the distributions to an RKHS are injective). Finally, we investigate the performance of this family of kernels in two-sample and independence tests: we show in particular that the energy distance most commonly employed in statistics is just one member of a parametric family of kernels, and that other choices from this family can yield more powerful tests.

Social influence has no small effect on the preferences and behavior of agents in a social space. Contrary to rationality, we sometimes compromise our own needs for those of others. Thus, social influence has important implications in agent cognitive modeling for multi-objective decision-making problems. Namely, where these activities occur within a social context, the intentional preferences or utility of an agent may be subsumed, to a greater or lesser degree, by the influences of other agents. In this paper, a socially-aware model predictive controller is proposed using a social influence network theory and applied to a HVAC control problem. It transforms individual agent utility to socially-influenced utility reflecting interagent influences due to their existing relationships.

In the Silver Anniversary year of FLAIRS, in an effort to promote discussion of emerging ideas and work in order to encourage and help guide researchers, especially new researchers, the program committee added the poster abstract submission category. This allows researchers to present a full poster in the conference poster session and receive that critical, work-shaping feedback that helps guide good work into great work.

We study the computational complexity of optimal bribery and manipulation schemes for sports tournaments with uncertain information: cup; challenge or caterpillar; and round robin. Our results carry over to the equivalent voting rules: sequential pair-wise elections, cup, and Copeland, when the set of candidates is exactly the set of voters. This restriction creates new difficulties for most existing algorithms. The complexity of bribery and manipulation are well studied, almost always assuming deterministic information about votes and results. We assume that for candidates i and j the probability that i beats j and the costs of lowering each probability by fixed increments are known to the manipulators. We provide complexity analyses for cup, challenge, and round robin competitions ranging from polynomial time to NP^PP. This shows that the introduction of uncertainty into the reasoning process drastically increases the complexity of bribery problems in some instances.

Most of preference representation languages developed in the literature are based on comparative preference statements. The latter offer a simple and intuitive way for expressing preferences. They can be interpreted following different semantics. This paper presents a postulate-based analysis of the different semantics describing their behavior w.r.t. three criteria: coherence, syntax independence and inference.

Children with cerebral palsy have difficulty moving their hands and muscles due to developmental issues. One way to assist these children is by having them participate in physical therapy. The best form of physical therapy for children is playing. Playing is a natural activity for children, and it also helps in furthering the developments of muscles. This form of therapy is perhaps a greater choice for children because it keeps the child engaged due to the interest the child holds in the activity. By integrating two projects done by previous students, a Pleo that is controlled by a Wii Nunchuk will be able to teach Pleo how to dance. The child will be engaged in this activity for long durations because there are many variations of dance that the Pleo can learn by moving many body parts. Children using this toy will have continuous movement in their arm muscles by moving the Nunchuk for the duration of the activity. This toy will not only help children with severe disabilities feeling equal to their non-disabled peers by allowing them to use controllers found on many game consoles, but it will also enhance the child’s self-esteem and confidence by allowing them to control the outcome of the Pleo.

In recent years there has been a significant decline in the number of college students choosing majors in computer science or technology related fields. Although this trend is beginning to turn around at the undergraduate level, there remains disparity in the number of under-represented minority students who earn graduate degrees as compared to majority students. Additionally, within the United States, there is an achievement gap between under-represented minority students and majority students at a time when underrepresented groups are becoming an increasing proportion of the national labor force. This reluctance to study Science, Technology, Engineering, and Mathematics (STEM) disciplines must be confronted and changed if the United States is to maintain a competitive position within the global market. Effective use of learning technologies is vital to solving many of our current STEM learning challenges. Robotics is a growing research area in computer science education. We use robotics as a technology tool captivate and engage students in research in robotics.

Calliope is an open source mobile robot designed in the Tekkotsu Lab at Carnegie Mellon University in collaboration with RoPro Design, Inc. The Calliope5SP model features an iRobot Create base, an ASUS netbook, a 5-degree of freedom arm with a gripper with two independently controllable fingers, and a Sony PlayStation Eye camera and Robotis AX-S1 IR rangefinder on a pan/tilt mount. We use chess as a test of Calliope’s abilities. Since Calliope is a mobile platform we consider how problems in vision and localization directly impact the performance of manipulation. Calliope’s arm is too short to reach across the entire chessboard. The robot must therefore navigate to a location that provides the best position to access the pieces it wants to move. The robot proved capable of performing small-scale manipulation tasks that require careful positioning.

Search algorithms such as Rapidly-exploring Random Trees (RRTs) are common in robot programming. Including graphical representations of the output of these algorithms in a robotics framework can make the algorithms more accessible to students, and can also help programmers analyze and account for unexpected results. For this project, we used the Tekkotsu open source robot programming framework, available at Tekkotsu.org. We extended Tekkotsu’s graphical user interface for displaying vision data and maps to also display the output of an RRT search. We created several demos using two types of searches: one from a navigation path planner, and one from an arm path planner. In some cases the search had no solution, and the graphical output helped to illustrate why. This confirms the utility of the RRT visualization for explaining unexpected search results. We expect that this tool will also contribute to improved student understanding of the search algorithm.

Robotics is a multidisciplinary area of study across computer science, electrical engineering, and mechanical engineering. Robotics covers the study, design, manufacture, and use of robots in various applications. Robotics, computer vision, activity recognition, path planning, and the many other disciplines where computers interface to physical environments have proven to be a major source of inspiration and crucial new insights into artificial intelligence. Human-robot interaction has become a major concern as many robots have been used in real-world applications. For this year, this track features a number of robotics projects of Summer Research Experience for Undergraduate from the Advancing Robotics Technology for Societal Impact Alliance.