Discovering Dialog Rules by means of an Evolutionary Approach

Designing the rules for the dialog management process is oneof the most resources-consuming tasks when developing a dialog system. Although statistical approaches to dialog management are becoming mainstream in research and industrial contexts, still many systems are being developed following the rule-based or hybrid paradigms. For example, when developers require deterministic system responses to keep total control on the decisions made by the system, or because the infrastructure employed is designed for rule-based systems using technologies currently used in commercial platforms. In this paper, we propose the use of evolutionary algorithms to automatically obtain the dialog rules that are implicit in a dialog corpus. Our proposal makes it possible to exploit the benefits of statistical approaches to build rule-based systems. Our proposal has been evaluated with a practical spoken dialog system, for which we have automatically obtained a set of fuzzy rules to successfully manage the dialog.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823907 (MENHIR project:https://menhir-project.eu

From sentence to emotion: a real-time three-dimensional graphics metaphor of emotions extracted from text

This paper presents a novel concept: a graphical representation of human emotion extracted from text sentences. The major contributions of this paper are the following. First, we present a pipeline that extracts, processes, and renders emotion of 3D virtual human (VH). The extraction of emotion is based on data mining statistic of large cyberspace databases. Second, we propose methods to optimize this computational pipeline so that real-time virtual reality rendering can be achieved on common PCs. Third, we use the Poisson distribution to transfer database extracted lexical and language parameters into coherent intensities of valence and arousal—parameters of Russell's circumplex model of emotion. The last contribution is a practical color interpretation of emotion that influences the emotional aspect of rendered VHs. To test our method's efficiency, computational statistics related to classical or untypical cases of emotion are provided. In order to evaluate our approach, we applied our method to diverse areas such as cyberspace forums, comics, and theater dialog

HUMAN ROBOT INTERACTION THROUGH SEMANTIC INTEGRATION OF MULTIPLE MODALITIES, DIALOG MANAGEMENT, AND CONTEXTS

The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot

Core Challenges in Embodied Vision-Language Planning

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.Comment: 35 page

From Sentence to Emotion: a real-time 3D graphics metaphor of emotions extracted from text

This paper presents a novel concept: a graphical representation of human emotion extracted from text sentences. The major contributions of this paper are the following. First, we present a pipeline that extracts, processes, and renders emotion of 3D virtual human (VH). The extraction of emotion is based on data mining statistic of large cyberspace databases. Second, we propose methods to optimize this computational pipeline so that real-time virtual reality rendering can be achieved on common PCs. Third, we use the Poisson distribution to transfer database extracted lexical and language parameters into coherent intensities of valence and arousal—parameters of Russell’s circumplex model of emotion. The last contribution is a practical color interpretation of emotion that influences the emotional aspect of rendered VHs. To test our method’s efficiency, computational statistics related to classical or untypical cases of emotion are provided. In order to evaluate our approach, we applied our method to diverse areas such as cyberspace forums, comics, and theater dialogs

The Duty of Responsible Administration and the Problem of Police Accountability

Many contemporary civil rights claims arise from institutional activity that, while troubling, is neither malicious nor egregiously reckless. When law-makers find themselves unable to produce substantive rules for such activity, they often turn to regulating the actors’ exercise of discretion. The consequence is an emerging duty of responsible administration that requires managers to actively assess the effects of their conduct on civil rights values and to make reasonable efforts to mitigate harm to protected groups. This doctrinal evolution partially but imperfectly converges with an increasing emphasis in public administration on the need to reassess routines in the light of changing circumstances. We illustrate the doctrinal and administrative changes with a study of policing. We discuss court-supervised reforms in New York and Cincinnati as examples of contrasting trajectories that these developments can take. Both initiatives are better understood in terms of an implicit duty of responsible administration than as an expression of any particular substantive right. However, the Cincinnati intervention reaches more deeply into core administrative practices and indeed mandates a particular crime control strategy – Problem-Oriented Policing. As such, it typifies a more ambitious type of structural civil-rights intervention that parallels comprehensive civil-rights initiatives in other areas

The Duty of Responsible Administration and the Problem of Police Accountability

Many contemporary civil rights claims arise from institutional activity that, while troubling, is neither malicious nor egregiously reckless. When law-makers find themselves unable to produce substantive rules for such activity, they often turn to regulating the actors’ exercise of discretion. The consequence is an emerging duty of responsible administration that requires managers to actively assess the effects of their conduct on civil rights values and to make reasonable efforts to mitigate harm to protected groups. This doctrinal evolution partially but imperfectly converges with an increasing emphasis in public administration on the need to reassess routines in the light of changing circumstances. We illustrate the doctrinal and administrative changes with a study of policing. We discuss court-supervised reforms in New York and Cincinnati as examples of contrasting trajectories that these developments can take. Both initiatives are better understood in terms of an implicit duty of responsible administration than as an expression of any particular substantive right. However, the Cincinnati intervention reaches more deeply into core administrative practices and indeed mandates a particular crime control strategy – Problem-Oriented Policing. As such, it typifies a more ambitious type of structural civil-rights intervention that parallels comprehensive civil-rights initiatives in other areas

Machine Learning from Casual Conversation

Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human\u27s conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research that is focused on teaching specific tasks to computer agents. Furthermore, LCC aims to provide an easy way to enhance the knowledge of the system without requiring the involvement of a programmer. This system does not require the user to enter specific information; instead, the user can chat naturally with the agent. LCC identifies the inputs that contain information relevant to its knowledge base in the learning process. LCC\u27s architecture consists of multiple sub-systems combined to perform the task. Its learning component can add new knowledge to existing information in the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. %The test results indicate that the prototype was successful in learning from a conversation. The LCC system functionality was assessed using different evaluation methods. This includes tests performed by the developer, as well as by 130 human test subjects. Thirty of those test subjects interacted directly with the system and completed a survey of 13 questions/statements that asked the user about his/her experience using LCC. A second group of 100 human test subjects evaluated the dialogue logs of a subset of the first group of human testers. The collected results were all found to be acceptable and within the range of our expectations

The self in the mirror: fostering researchers’ reflexivity in transdisciplinary and transformative studies at the science-policy interface

Reflexivity is a key expectation that researchers in transdisciplinary and transformative research for sustainable development need to meet. Its aim is to enable researchers to deal with normativity, to contribute to identifying and balancing different actors’ interests in processes of knowledge production, and to strengthen a pluralistic view of implicit assumptions. When designing and realizing transdisciplinary and transformative studies, researchers face a central question: How can we develop reflexive practices and live up to the demands of such work? Considering the important role that reflexivity plays in transdisciplinary approaches, it is surprising that only few approaches have explored the specific characteristics of reflexive practices empirically and analyzed how these practices are cultivated when doing transdisciplinary and transformative research. In this article we address this research gap by presenting and discussing a case in which researchers attempted to professionalize their reflexive practices at the science-policy interface (SPI). As part of the national Monitoring of Education for Sustainable Development in Germany, we used the method of collaborative autoethnography to systematically reflect on our own thinking and actions as researchers at the SPI over a period of 11 months. Based on an analysis of 66 situations in which we took field notes, we synthesized core topics of reflection and challenges encountered throughout the process (roles, relationship patterns, and normativity) in six collaborative interpretation sessions and analyzed them to understand our own practices of engagement within the field. Grounded in this analysis of our own selves as researchers looking in the mirror, we develop hypotheses about how our specific methodological approach helped us on a practical level to foster different kinds of reflexivity. With this two-fold approach, we aim to contribute to a better understanding of possible topics, challenges, and pathways of (increased) reflexivity among researchers working at the SPI