We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing that our Bayesian strategies are effective even in large concept spaces with many uninformative experts.

This article studies Frequency Adjusted Q-learning (FAQ-learning), a variation of Q-learning that simulates simultaneous value function updates. The main contributions are empirical and theoretical support for the convergence of FAQ-learning to attractors near Nash equilibria in two-agent two-action matrix games.The games can be divided into three types: Matching pennies, Prisoners' Dilemma and Battle of Sexes. This article shows that the Matching pennies and Prisoners' Dilemma yield one attractor of the learning dynamics, while the Battle of Sexes exhibits a supercritical pitchfork bifurcation at a critical temperature, where one attractor splits into two attractors and one repellent fixed point. Experiments illustrate that the distance between fixed points of the FAQ-learning dynamics and Nash equilibria tends to zero as the exploration parameter of FAQ-learning approaches zero.

This extended abstract outlines a new study that investigates spatial language for use in human-robot communication. The scenario studied is a home setting in which the elderly resident has misplaced an object, such as eyeglasses, and the robot will help the resident find the object. We present preliminary results from the initial study in which we investigate spatial language generated to a human addressee or a robot addressee in a virtual environment.

Robot factors such as motions and utterances have a possibility of interaction effects with generation and other human factors, and these effects influence robotics design in elder care. Some psychological experiments conducted in our research group found these interaction effects between generation and other factors based on directly comparison between younger and elder persons in interaction with a small-sized humanoid robot. The paper firstly reviews the previous two studies, reports results of the current experiment, and then discusses about their implications from the perspective of robotics design for elder care.

A general perceptual model is proposed for Eldercare Robot implementation that is comprised of audition functionality interconnected with a feedback-driven perceptual reasoning agent. Using multistage signal analysis to feed temporally tiered learning/recognition modules, concurrent access to sound event localization, classification, and context is realized. Patterns leading to the quantification of patient emotion/well being can be inferred using a perceptual reasoning agent. The system is prototyped using a Nao H-25 humanoid robot with an online processor running the Nao Qi SDK and the Max/MSP environment with the FTM, and GF libraries.

The creation of high fidelity scenarios for disaster simulation is a major challenge for a number of reasons. First, the maps supplied by existing map providers tend to provide only road or building shapes and do not accurately model open spaces which people use to evacuate buildings, homes, or industrial facilities. Secondly, even if some of the data about evacuation routes is available, the real-world connection points between these spaces and roads and buildings is usually not well defined unless data from buildings’ owners can be obtained. Finally, in order to augment current maps with accurate spatial data, it would require either a good set of training data for a computer vision algorithm to define evacuation routes using pictures or a significant amount of manpower to directly survey a vast area. Against this background, we develop a novel model of geospatial data creation, called CollabMap, that relies on human computation. CollabMap is a crowdsourcing tool to get users contracted via Amazon Mechanical Turk or a similar service to perform micro-tasks that involve augmenting existing maps by drawing evacuation routes, using satellite imagery from Google Maps and panoramic views from Google Street-View. We use human computation to complete tasks that are hard for a computer vision algorithm to perform or to generate training data that could be used by a computer vision algorithm to automatically define evacuation routes.

Crowdsourcing markets such as Amazon’s Mechanical Turk provide an enormous potential for accomplishing work by combining human and machine computation. Today crowdsourcing is mostly used for massive parallel information processing for a variety of tasks such as image labeling. However, as we move to more sophisticated problem-solving there is little knowledge about managing dependencies between steps and a lack of tools for doing so. As the contribution of this paper, we present a concept of an executable, model-based programming language and a general purpose framework for accomplishing more sophisticated problems. Our approach is inspired by coordination theory and an analysis of emergent collective intelligence. We illustrate the applicability of our proposed language by combining machine and human computation based on existing interaction patterns for several general computation problems.

In this paper, we develop a new human computation algorithm for speech-to-text transcription that can potentially achieve the high accuracy of professional transcription using only microtasks deployed via an online task market or a game. The algorithm partitions audio clips into short 10-second segments for independent processing and joins adjacent outputs to produce the full transcription. Each segment is sent through an iterative dual pathway structure that allows participants in either path to iteratively refine the transcriptions of others in their path while being rewarded based on transcriptions in the other path, eliminating the need to check transcripts in a separate process. Initial experiments with local subjects show that produced transcripts are on average 96.6% accurate.

Using these custom tools requires have been busily converting material from paper and microfilm training and a skilled workforce. We show in this paper that into digital domain. Newspapers, books, journals and some parts of that process can be distributed to a pool of even individual letters are finding themselves inside large unskilled volunteers with good results.

Micro-task markets like Amazon MTurk enable online workers to provide human intelligence as Web-based on demand services (so called "people services"). Businesses facing large amounts of knowledge work can benefit from increased flexibility and scalability of their workforce but need to cope with reduced control of result quality. While this problem is well recognized, it has so far only rudimentarily been addressed by existing platforms and tools. In this paper, we present a flexible research toolkit which enables experiments with advanced quality management mechanisms for generic micro-task markets. The toolkit enables control of correctness and performance of task fulfillment by means of continuous sampling, dynamic majority voting and worker pooling. While we demonstrate its application and performance for an OCR scenario building on Amazon MTurk, the toolkit supports the development of advanced quality management mechanisms for a large variety of people service scenarios and platforms.