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.

It is well known that argumentation can usefully be analysed as a distinct, if complex, type of speech act. Speech acts that form a part of argumentative discourse, and in particular, of argumentative dialogue, can be seen as anchors for the establishment of inferences between propositions in the domain of discourse. Most often, the speech acts that directly give rise to inference are implicit, but can be drawn out in analysis by consideration of the type of dialogue game being played. AI approaches to argumentation often focus solely on such inferences as the means by which persuasion can be effected – but this is in contrast with psychological and rhetorical models which have long recognised the role played by extra-logical features of the dialogical context. These ‘peripheral’ cues can not only affect persuasive effect of the logical, ‘central’ argumentation, but can override and dominate it. This paper presents a theory which allows both central and peripheral aspects of argumentation to be represented in a coherent analytical account based on the sequences of speech acts which constitute dialogues.

Despite recent impressive progress in automated planning and navigation tools, artifacts still lack robustness and flexibility of biological systems. In order to mimic biology, it is necessary to use principles of dynamics and architecture found in the brain. Here we translate our biologically inspired model of spatial learning and navigation (Samsonovich and Ascoli, L&M 2005) into a model suitable for implementation in spiking networks with STDP synapses, based on soon to become available hardware. Simulation studies of the model prove its robustness and scalability. The approach naturally extends to various types of action planning beyond the spatial domain. The architecture can be used in autonomous intelligent agents of various nature.

Current methods for multi-AUV cooperation suffer in low communication environments. State of the art methods employ auctioneering or planning to determine a single AUV'task. These systems require communication to update models of teammates and tasks for efficient task selection. Most strategies assume a teammate is inoperable if a communication timeout is reached which reduces overall team efficiency. Including teammate prediction has been shown to mitigate efficiency degeneration due to low communication. However, there is no verification of a predicted teammate's task other than through eventual communication. A possible verification tool is behavior recognition. Current behavior recognition utilizes either overhead sensors or post mission analysis to track robot trajectories in order to infer their internal state. A system in which an AUV is capable of sensing a teammate, for example through a forward-looking sonar, and deducing it's behavior along with contextual information, such as location, will enable an AUV to determine that teammate's current task in the overall mission. This will allow for an accurate update of that teammate's model allowing the AUV to more efficiently determine its own next task rather than relying only on communication. This position paper posits that multi-AUV cooperation efficiency will improve in low communication environments with the combination of robust teammate prediction along with verification using behavior recognition.

In this paper we describe the integration of a robot control platform (Player/Stage) and a real robot (Pioneer P3DX) with PELEA (Planning, Execution and LEarning Architecture). PELEA is a general-purpose planning architecture suitable for a wide range of real world applications, from robotics to emergency management. It allows planning engineers to generate planning applications since it integrates planning, execution, replanning, monitoring and learning capabilities. We also present a relational learning approach for automatically modeling robot-action execution durations, with the purpose of improving the planning process of PELEA by refining domain definitions.

Reasoning agents are often faced with the need to robustly deal with erroneous information. When a robot given the task of returning with the red cup from the kitchen table arrives in the kitchen to find no red cup but instead notices a blue cup and a red plate on the table, what should it do? The best course of action is to attempt to salvage the situation by relying on its preferences to return with one of the objects available. We provide a solution to this problem using the Situation Calculus extended with a notion of belief. We then provide an efficient practical implementation by mapping this formalism into default rules for which we have an implemented solver.

We present our efforts to deploy mobile robots in office environments, focusing in particular on the challenge of planning a schedule for a robot to accomplish user-requested actions. We concretely aim to make our CoBot mobile robots available to execute navigational tasks requested by users, such as telepresence, and picking up and delivering messages or objects at different locations. We contribute an efficient web-based approach in which users can request and schedule the execution of specific tasks. The scheduling problem is converted to a mixed integer programming problem. The robot executes the scheduled tasks using a synthetic speech and touch-screen interface to interact with users, while allowing users to follow the task execution online. Our robot uses a robust Kinect-based safe navigation algorithm, moves fully autonomously without the need to be chaperoned by anyone, and is robust to the presence of moving humans, as well as non-trivial obstacles, such as legged chairs and tables. Our robots have already performed 15km of autonomous service tasks.

Mobile robots are increasingly being used in the real-world due to the availability of high-fidelity sensors and sophisticated information processing algorithms. A key challenge to the widespread deployment of robots is the ability to accurately sense the environment and collaborate towards a common objective. Probabilistic sequential decision-making methods can be used to address this challenge because they encapsulate the partial observability and non-determinism of robot domains. However, such formulations soon become intractable for domains with complex state spaces that require real-time operation. Our prior work enabled a mobile robot to use hierarchical partially observable Markov decision processes (POMDPs) to automatically tailor visual sensing and information processing to the task at hand. This paper introduces adaptive observation functions and policy re-weighting in a three-layered POMDP hierarchy to enable reliable and efficient visual processing in dynamic domains. In addition, each robot merges its beliefs with those communicated by teammates, to enable a team of robots to collaborate robustly. All algorithms are evaluated in simulated domains and on physical robots tasked with locating target objects in indoor environments.