Annotation of negotiation processes in joint-action dialogues

Situated dialogic corpora are invaluable resources for understanding the complex relationship between language, perception, and action as they are based on naturalistic dialogue situations in which the interactants are given shared goals to be accomplished in the real world. In such situations, verbal interactions are intertwined with actions, and shared goals can only be achieved via dynamic negotiation processes based on common ground constructed from discourse history as well as the interactants' knowledge about the status of actions. In this paper, we propose four major dimensions of collaborative tasks that affect the negotiation processes among interactants, and, hence, the structure of the dialogue. Based on a review of available dialogue corpora and annotation manuals, we show that existing annotation schemes so far do not adequately account for the complex dialogue processes in situated task-based scenarios. We illustrate the effects of specific features of a scenario using annotated samples of dialogue taken from the literature as well as our own corpora, and end with a brief discussion of the challenges ahead

USA Rail Planner: A user-focused web-scraping solution for rail travel planning in the United States

Planning a cross-country train journey in the United States can be a time-consuming process. The USA Rail Planner, presented in this thesis, provides travelers an easy way to plan a multi-city rail trip to any of the destinations served by Amtrak trains in the United States. The manual work of searching the Amtrak website and inputting information into a spreadsheet is no longer necessary. By interfacing with the website, information can be parsed by the application quickly and presented to the user in a simpler, ordered, and less cluttered format, allowing them to make educated decisions in their trip planning process. Dynamic route maps, detailed train information, and many other planning features are present in the application. Quality-of-life additions, such as train timetables, city tourism pages, and local transit connections, make the application well-rounded in the tourism and travel domains. Furthermore, this user-centered Python-based application that employs web scraping and other modern software technologies provides an efficient and easy way to create an itinerary which can be exported later. User study results (N=12) show that the USA Rail Planner is significantly better than existing methods, reducing the time to create an itinerary by 47% and it was the preferred method for all but one participant

Unemployment and occupational mobility at the beginning of employment career in Germany and the UK

"The beginning of the employment career is often associated with phases of unemployment. We argue that unemployment has different implications for different educational groups on future employment career depending on institutional settings in the UK and Germany. While search and matching models argue that an unemployment phase might be used for an active job search and might result in a better position, human capital and signalling theory predict status losses. The strongly skill-based and rigid labour market in Germany creates a stigma attached to unemployment and therefore might have negative consequences upon the re-entry into the labour market for all educational groups. The 'trial and error' strategy at the beginning of an employment career in flexible labour markets is common and therefore search and matching models should predict positive outcomes in the UK, especially for high-educated persons. Using the German Socio-Economic Panel and British Household Panel we simultaneously estimate hazard rates and changes in the occupational status." (Author's abstract, IAB-Doku) ((en))Berufsanfänger, berufliche Mobilität - internationaler Vergleich, Arbeitslosigkeit - Auswirkungen, Arbeitslose, junge Erwachsene, berufliche Reintegration, Arbeitsmarktchancen, institutionelle Faktoren, Berufsverlauf, Stigmatisierung, Großbritannien, Bundesrepublik Deutschland

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Toward multi-target self-organizing pursuit in a partially observable Markov game

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve intelligent agents' search and pursuit capabilities. We model a self-organizing system as a partially observable Markov game (POMG) with the features of decentralization, partial observation, and noncommunication. The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that distributed noncommunicating multi-agent coordination with partial observations in all three subtasks are effective, and 2048 FSC2 agents can perform efficient multi-target SOP with almost 100% capture rates