Prototype of a Conversational Assistant for Satellite Mission Operations

The very first artificial satellite, Sputnik, was launched in 1957 marking a new era. Concurrently, satellite mission operations emerged. These start at launch and finish at the end of mission, when the spacecraft is decommissioned. Running a satellite mission requires the monitoring and control of telemetry data, to verify and maintain satellite health, reconfigure and command the spacecraft, detect, identify and resolve anomalies and perform launch and early orbit operations. The very first chatbot, ELIZA was created in 1966, and also marked a new era of Artificial Intelligence Systems. Said systems answer users’ questions in the most diverse domains, interpreting the human language input and responding in the same manner. Nowadays, these systems are everywhere, and the list of possible applications seems endless. The goal of the present master’s dissertation is to develop a prototype of a chatbot for mission operations. For this purpose implementing a Natural Language Processing (NLP) model for satellite missions allied to a dialogue flow model. The performance of the conversational assistant is evaluated with its implementation on a mission operated by the European Space Agency (ESA), implying the generation of the spacecraft’s Database Knowledge Graph (KG). Throughout the years, many tools have been developed and added to the systems used to monitor and control spacecrafts helping Flight Control Teams (FCT) either by maintaining a comprehensive overview of the spacecraft’s status and health, speeding up failure investigation, or allowing to easily correlate time series of telemetry data. However, despite all the advances made which facilitate the daily tasks, the teams still need to navigate through thousands of parameters and events spanning years of data, using purposely built user interfaces and relying on filters and time series plots. The solution presented in this dissertation and proposed by VisionSpace Technologies focuses on improving operational efficiency whilst dealing with the mission’s complex and extensive databases.O primeiro satélite artificial, Sputnik, foi lançado em 1957 e marcou o início de uma nova era. Simultaneamente, surgiram as operações de missão de satélites. Estas iniciam com o lançamento e terminam com desmantelamento do veículo espacial, que marca o fim da missão. A operação de satélites exige o acompanhamento e controlo de dados de telemetria, com o intuito de verificar e manter a saúde do satélite, reconfigurar e comandar o veículo, detetar, identificar e resolver anomalias e realizar o lançamento e as operações iniciais do satélite. Em 1966, o primeiro Chatbot foi criado, ELIZA, e também marcou uma nova era, de sistemas dotados de Inteligência Artificial. Tais sistemas respondem a perguntas nos mais diversos domínios, para tal interpretando linguagem humana e repondendo de forma similar. Hoje em dia, é muito comum encontrar estes sistemas e a lista de aplicações possíveis parece infindável. O objetivo da presente dissertação de mestrado consiste em desenvolver o protótipo de um Chatbot para operação de satélites. Para este proposito, criando um modelo de Processamento de Linguagem Natural (NLP) aplicado a missoões de satélites aliado a um modelo de fluxo de diálogo. O desempenho do assistente conversacional será avaliado com a sua implementação numa missão operada pela Agência Espacial Europeia (ESA), o que implica a elaboração do grafico de conhecimentos associado à base de dados da missão. Ao longo dos anos, várias ferramentas foram desenvolvidas e adicionadas aos sistemas que acompanham e controlam veículos espaciais, que colaboram com as equipas de controlo de missão, mantendo uma visão abrangente sobre a condição do satélite, acelerando a investigação de falhas, ou permitindo correlacionar séries temporais de dados de telemetria. No entanto, apesar de todos os progressos que facilitam as tarefas diárias, as equipas ainda necessitam de navegar por milhares de parametros e eventos que abrangem vários anos de recolha de dados, usando interfaces para esse fim e dependendo da utilização de filtros e gráficos de series temporais. A solução apresentada nesta dissertação e proposta pela VisionSpace Technologies tem como foco melhorar a eficiência operacional lidando simultaneamente com as suas complexas e extensas bases de dados

On cross-domain social semantic learning

Approximately 2.4 billion people are now connected to the Internet, generating massive amounts of data through laptops, mobile phones, sensors and other electronic devices or gadgets. Not surprisingly then, ninety percent of the world's digital data was created in the last two years. This massive explosion of data provides tremendous opportunity to study, model and improve conceptual and physical systems from which the data is produced. It also permits scientists to test pre-existing hypotheses in various fields with large scale experimental evidence. Thus, developing computational algorithms that automatically explores this data is the holy grail of the current generation of computer scientists. Making sense of this data algorithmically can be a complex process, specifically due to two reasons. Firstly, the data is generated by different devices, capturing different aspects of information and resides in different web resources/ platforms on the Internet. Therefore, even if two pieces of data bear singular conceptual similarity, their generation, format and domain of existence on the web can make them seem considerably dissimilar. Secondly, since humans are social creatures, the data often possesses inherent but murky correlations, primarily caused by the causal nature of direct or indirect social interactions. This drastically alters what algorithms must now achieve, necessitating intelligent comprehension of the underlying social nature and semantic contexts within the disparate domain data and a quantifiable way of transferring knowledge gained from one domain to another. Finally, the data is often encountered as a stream and not as static pages on the Internet. Therefore, we must learn, and re-learn as the stream propagates. The main objective of this dissertation is to develop learning algorithms that can identify specific patterns in one domain of data which can consequently augment predictive performance in another domain. The research explores existence of specific data domains which can function in synergy with another and more importantly, proposes models to quantify the synergetic information transfer among such domains. We include large-scale data from various domains in our study: social media data from Twitter, multimedia video data from YouTube, video search query data from Bing Videos, Natural Language search queries from the web, Internet resources in form of web logs (blogs) and spatio-temporal social trends from Twitter. Our work presents a series of solutions to address the key challenges in cross-domain learning, particularly in the field of social and semantic data. We propose the concept of bridging media from disparate sources by building a common latent topic space, which represents one of the first attempts toward answering sociological problems using cross-domain (social) media. This allows information transfer between social and non-social domains, fostering real-time socially relevant applications. We also engineer a concept network from the semantic web, called semNet, that can assist in identifying concept relations and modeling information granularity for robust natural language search. Further, by studying spatio-temporal patterns in this data, we can discover categorical concepts that stimulate collective attention within user groups.Includes bibliographical references (pages 210-214)

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

A robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts