To assist scientists in data analysis tasks, we have developed semantic workflow representations that support automatic constraint propagation and reasoning algorithms to manage constraints among the individual workflow steps. Semantic constraints can be used to represent requirements of input datasets as well as best practices for the method represented in a workflow. We demonstrate how the Wings workflow system uses semantic workflows to assist users in creating workflows while validating that the workflows comply with the requirements of the software components and datasets. Wings reasons over semantic workflow representations that consist of both a traditional dataflow graph as well as a network of constraints on the data and components of the workflow.

In order to design robots suited to engage in cooperative manipulation tasks with humans, we study human-human teams as they work together to move a heavy object across a room. We are interested in several questions. First, do two person, gross motion tasks follow the same sinusoidal pattern, one person fine motion tasks do? Does performance improve when audio or visual communication is permitted? How does performance correlate with an individual's perception of performance? Non-physiological, or performance based, studies of human-human cooperation can be divided into two categories: Haptic and Non-Haptic (audio, visual, etc). The first category, involves physical interaction through the object being manipulated via force, pressure, and tactile sensations (Jones and Sarter 2008), (Reed and Peshkin 2008). Most of the non-haptic experiments are virtual setups where individuals are moving an object together on a computer screen via two controllers (Basdogan, Ho, and Srinivasan 2000), (Sallnas and Zhai 2003). A survey on the role of communication between people appears in (Whitaker, 2003). The novelty of our work is to investigate non-haptic communication in haptic manipulation tasks.

The useful This paper reports on the ongoing work done in the information is obviously multilevel. In this work we are GVLEX project. The aim of this multidisciplinary project not willing to design complete analysis for each level of is to design and test a storytelling humanoid robot. Ideally, interest but rather to design a multilevel analysis able to the robot would be able to process automatically a given point out the interesting parts of the tale. Based on the tale or short story, and to play it for a children audience.

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as 'Place the cup down (on the table)," potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.

This paper summarizes our previous works in modeling non-verbal behaviors for natural human-robot interaction (HRI) and discusses a path for integrating them into spoken dialogs. While some non-verbal behaviors can be considered “optional” elements to be added to a spoken dialog, some non-verbal behaviors substantially require a harmonized plan that simultaneously considers both spoken dialog and non-verbal behavior. The paper discusses such unique HRI features.

We propose a computational model of joint attention consisting of three parts: responding to joint attention, initiating joint attention, and ensuring joint attention. This model is supported by psychological findings and matches the developmental timeline in humans. We present two experiments that test this model and investigate joint attention in human-robot interaction. The first experiment explored the effects of responding to joint attention on human-robot interaction. We show that robots responding to joint attention are more transparent to humans and are more competent and socially interactive. The second experiment studied the importance of ensuring joint attention in human-robot interaction. Data upheld our hypotheses that a robot's ensuring joint attention behavior yields better performance in human-robot interactive tasks and ensuring joint attention behaviors are perceived as natural behaviors.

Many theories of discourse structure rely on the idea that the segments comprising the discourse are linked through inferred relations such as causality and temporal contiguity. These theories suggest that the resulting discourse is represented hierarchically. Two experiments examine some of the implications of these hierarchical structures on the perceived coherence of texts. Experiment 1 shows that texts with more levels to their hierarchical structure are judged to be more coherent. Experiment 2 demonstrates that these effects are sensitive to the genre of the text. Specifically, narratives seem to be more affected by manipulation of the discourse structure than procedural texts.

How do reasoning systems that learn evolve over time? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how learning and reasoning interact: Create a small knowledge base by ablation, and incrementally re-add facts, collecting snapshots of reasoning performance of the system to measure properties of interest. Experiments with this model suggest that different concepts show different rates of growth, and that the density of facts is an important parameter for modulating the rate of learning.

Collecting commonsense from text with the aid of a game can reduce the cost and effort of creating large knowledge bases. In this paper, we design, implement, and evaluate an online game that classifies, with input from players, text extracted from the Web as commonsense knowledge, domain-specific knowledge or nonsense. We also create a knowledge base that includes commonsense facts in natural language and information on how common a given fact is. The game is currently available for play on the Web and on Facebook, and under constant improvement. The creation of a continuous scale to classify commonsense helped during evaluation of the data by clearly identifying which knowledge is reliable and which needs further qualification. When comparing our results to other similar knowledge acquisition systems, our Turing Game performs better with respect to coverage,redundancy, and reliability of the commonsense acquired.

Games With A Purpose (or GWAP) provide an interesting way to collect data from web users. With over a million sentences collected and growing steadily, data verification becomes increasingly important. This research explores the alternative of designing human computation games specifically for verification purposes. Two games, Top10 and Pirate and Ghost, are designed for commonsense data verification. Top10 is a single-player game, in which the player attempts to guess the top answers to a given question. We use the frequency data to verify if the assertion is truly common. Pirate and Ghost is a multiplayer guessing role playing game in a network of concepts from the CSKB. We use the game data to identify the relation between two concepts. This paper presents the design of both games, and evaluate the efficiency and precision of each with two experiments. The results show that the two games can be coupled to achiever higher efficiency and precision in the data verification process.