The primary goal of our research is to design adaptive information visualization systems that adapt to the specific needs of each individual viewer. Our first step is to explore data sources that could help detect these needs in real-time, including cognitive measures that impact perceptual abilities, interface interactions, eye-tracking, and physiological sensors. In this paper, we focus on current efforts to understand which cognitive measures can be relevant, as well as if/how a viewer’s gaze pattern can predict performance on associated visualization task.

Many practitioners currently use rules of thumb to price tasks on online labor markets. Incorrect pricing leads to task starvation or inefficient use of capital. Formal pricing policies can address these challenges. In this paper we argue that a pricing policy can be based on the trade-off between price and desired completion time.We show how this duality can lead to a better pricing policy for tasks in online labor markets. This paper makes three contributions. First, we devise an algorithm for job pricing using a survival analysis model. We then show that worker arrivals can be modeled as a non-homogeneous Poisson Process (NHPP). Finally using NHPP for worker arrivals and discrete choice models we present an abstract mathematical model that captures the dynamics of the market when full market information is presented to the task requester. This model can be used to predict completion times and pricing policies for both public and private crowds.

This paper reports the results of a natural field experiment where workers from a paid crowdsourcing environment self-select into tasks and are presumed to have limited attention. In our experiment, workers labeled any of six pictures from a 2 x 3 grid of thumbnail images. In the absence of any incentives, workers exhibit a strong default bias and tend to select images from the top-left (``focal'') position; the bottom-right (``non-focal'') position, was the least preferred. We attempted to overcome this bias and increase the rate at which workers selected the least preferred task, by using a combination of monetary and non-monetary incentives. We also varied the saliency of these incentives by placing them in either the focal or non-focal position. Although both incentive types caused workers to re-allocate their labor, monetary incentives were more effective. Most interestingly, both incentive types worked better when they were placed in the focal position and made more salient. In fact, salient non-monetary incentives worked about as well as non-salient monetary ones. Our evidence suggests that user interface and cognitive biases play an important role in online labor markets and that salience can be used by employers as a kind of ``incentive multiplier.''

We consider the problem of dynamic self-reconfiguration in a modular self-reconfigurable robot (MSR). Previous MSR self-reconfiguration approaches search for new configurations only within the modules of the MSR that needs reconfiguration. In contrast, we describe a technique where an MSR that needs to reconfigure communicates with other MSRs in its vicinity to determine if modules can be shared from other MSRs, and then determines the best possible configuration among the combined set of modules. We model the MSR self-reconfiguration problem as a coalition structure generation problem within a coalition game theoretic framework. We formulate the coalition structure generation problem as a planning problem in the presence of uncertainty and propose an MDP-based algorithm to solve it. We have implemented our algorithm within an MSR called ModRED that is simulated on the Webots simulation platform. Our results show that using our self-reconfiguration algorithm, when an MSR needs to reconfigure, a new configuration that is within 5-7% of the globally optimal configuration can be determined. We have also shown that our algorithm performs comparably with another existing algorithm for determining optimal coalition structure.

We present current work on the NextSlidePlease slide- ware presentation tool and discuss how mixed-initiative principles may support the complex tasks of combin- ing multiple linear presentations into a network of re- lated content. We discuss future directions in two areas: supporting the layout of complex sets of interconnected slides, and refining the time requirements and content importance in these networks.

On today's human computation systems, designing tasks and workflows is a difficult and labor-intensive process. Can workers from the crowd be used to help plan workflows? We explore this question with Turkomatic, a new interface to microwork platforms that uses crowd workers to help plan workflows for complex tasks. Turkomatic uses a general-purpose divide-andconquer algorithm to solve arbitrary natural-language requests posed by end users. The interface includes a novel real-time visual workflow editor that enables requesters to observe and edit workflows while the tasks are being completed. Crowd verification of work and the division of labor among members of the crowd can be handled automatically by Turkomatic, which substantially simplifies the process of using human computation systems. These features enable a novel means of interaction with crowds of online workers to support successful execution of complex work. Figure 1: Turkomatic harnesses crowds to plan and execute complex work requested in natural language.

In this paper, we propose an innovative approach for the development of social collaboration argumentation systems. These systems enable a community to collaboratively create answers to questions where many possible answers, or nuanced perspectives on a single answer, can be posited. We examine the emergence of critical reasoning via crowdsourced structured discussions, which are built upon a graph-theoretic framework populated by atomic argumentation components. Finally, we address the design of the online community to best facilitate this interaction. Our main contribution is the rationale and design of the system, which can easily be extended to build a general eLearning framework.

The major limitation to standard classification techniques is that the classifiers have to be trained on objects for which the ground truth, ACT-R contains a robust declarative memory module, which in terms of either a pre-assigned label or an error signal, is stores information as "chunks." A chunk in ACT-R may contain known. This limitation prevents the classifiers from dynamically any number of slots and values for those slots; slot values developing their own categories of classification based may be other chunks, numbers, strings, lists, or generally on information obtained from the environment. Previous attempts any data type allowed in Lisp (the base language for to overcome these limitations have been based on ACT-R). Retrieval from declarative memory is handled by a classical machine learning algorithms (Modayil and Kuipers request to the retrieval module; the request specifies the conditions 2007) (Kuipers et al. 2006). Here we present an alternative to be met in order for a chunk to be retrieved from approach to this problem, and develop the beginnings of declarative memory, and the module either returns a chunk a framework within which a classifier can evolve its own matching those specifications or generates a failure signal if representations based on dynamical information from the a retrieval cannot be made.

Continual learning is the unending process of learning new things on top of what has already been learned (Ring, 1994).Temporal Transition Hierarchies (TTHs) were developed to allow prediction of Markov-k sequences in a way that was consistent with the needs of a continual-learning agent (Ring, 1993).However, the algorithm could not learn arbitrary temporal contingencies.This paper describes Recurrent Transition Hierarchies (RTH), a learning method that combines several properties desirable for agents that must learn as they go.In particular, it learns online and incrementally, autonomously discovering new features as learning progresses.It requires no reset or episodes.It has a simple learning rule with update complexity linear in the number of parameters.

Human computing promises new capabilities that cannot be easily provided by computing machinery. However, humans are less disciplined than their mechanical counterparts and hence are liable to produce accidental or deliberate mistakes. As we start to develop regimes for identifying and correcting errors in human computation, we find an important model in the computing groups that operated at the start of the 20th century.