Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation

This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference

Geração automática de ontologias probabilísticas a partir de um modelo UMP-ST

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.O URP-ST é uma metodologia baseada no processo unificado que orienta o engenheiro de ontologias durante a construção de ontologias probabilísticas por meio de uma série de etapas que englobam desde a modelagem até a realização de inferências. A etapa de modelagem é definida pelo UMP-ST, uma metodologia iterativa e incremental voltada para a maioria das tecnologias semânticas. Uma delas é o PR-OWL, uma linguagem para a representação do MEBN. A modelagem de ontologias probabilísticas a partir do UMP-ST utilizando MEBN/PR-OWL pode ser realizada no UnBBayes, um framework para a construção gráfica de modelos probabilísticos e a realização de raciocínio plausível. Apesar da orientação dada pelo UMP-ST, a modelagem de ontologias probabilísticas é uma tarefa penosa e repetitiva. Durante a implementação do modelo, é necessário a construção da ontologia a partir do zero utilizando um determinada tecnologia semântica, além da modelagem feita no UMP-ST. Uma integração apropriada que ajude o usuário a implementar a ontologia, tal como um estrutura intermediária, agilizaria e facilitaria a sua implementação. Esse trabalho propõe um plug-in Java para o UnBBayes com o objetivo de automatizar o mapeamento de uma ontologia modelada via UMP ST em um modelo MEBN, permitindo ao usuário realizar inferências probabilísticas em ontologias com representação de conhecimento com ou sem incerteza probabilística.The URP-ST is a methodology based on the unified process that guides the ontology engineer in how to design Probabilistic Onologies. The UMP-ST is an incremental and iterative approach that covers the modeling step related to the URP-ST. It is a general methodology for the majority of the existing semantic technologies which support uncertainty. One of them is the PR-OWL, a language for MEBN representation. The modeling of probabilistic ontologies from the UMP-ST using MEBN / PR-OWL can be performed in UnBBayes, a framework for building probabilistic graphical models and performing plausible reasoning. Despite the guidance given by the UMP-ST, the implementation of a PO is a painful and repetitive task. During the implementation of the model, it is necessary to build the ontology from the zero using a specific semantic technology, even if the user models the PO in UMP-ST. A proper integration that helps the user to implement the PO, such as an intermediate structure, would expedite and facilitate its implementation. This work presents an automatic way to generate POs using MEBN representation from the UMP-ST model by mapping the elements of both sides. This is an extension of the UMP-ST to generate POs to an specific formalism and it is developed as a Java plug-in for UnBBayes

Modellierung und Analyse individuellen Konsumentenverhaltens mit probabilistischen Holonen

Der Schwerpunkt dieser Arbeit liegt in der Entwicklung eines agentenbasierten, probabilistischen Konsumentenverhaltensmodells zur Repräsentation und Analyse individuellen Kaufverhaltens. Das Modell dient zur Entscheidungsunterstützung im Handel und speziell im Customer Relationship Management (CRM). Als Modellgrundlage wird eine Klasse probabilistischer Agenten eingeführt, die sich zu Holonen zusammenschließen können und deren Wissensbasen erweiterte Bayes';sche Netze (Verhaltensnetze) sind. Mit Hilfe probabilistischer Holone werden Kundenagenten entwickelt, die einzelne reale Kundenmodellieren. Dazu werden kundenindividuelle Verhaltensmuster unter Berücksichtigung von Domänenwissen aus historischen Kundendaten extrahiert und als nichtlineare Abhängigkeiten zwischen Einflussfaktoren und artikelbezogenen Kundenreaktionen in Verhaltensnetzen repräsentiert. Ein Kundenagent ist dabei ein Holon aus mehreren so genannten Feature-Agenten, die jeweils einzelne Kundeneigenschaften repräsentieren, entsprechende Feature-Verhaltensnetze verwalten und durch Interaktion das Gesamtverhalten des Kunden bestimmen. Die Simulation des Verhaltens besteht aus der Ermittlung von Kundenreaktionen auf vorgegebene Einkaufsszenarien mit Hilfe quantifizierbarer probabilistischer Schlussfolgerungen. Kundenagenten können sich durch Holonisierung zu Kundengruppenagenten zusammenschließen, die unterschiedliche Aggregationen des Kaufverhaltens der Gruppenmitglieder repräsentieren. Zur Bestimmung gleichartiger Kunden werden auf Basis der Verhaltensnetze mehrere Ähnlichkeitsanalyseverfahren sowie verhaltensbezogene Ähnlichkeitsmaße zum Vergleich des dynamischen Kaufverhaltens entwickelt. Bestehende Klassifikations- und Clusteringverfahren werden anschließend so erweitert, dass sie neben klassischen Attributvektoren verhaltensnetzbasierte Repräsentationen als Vergleichsgrundlage verwenden können. Darüber hinaus werden Verfahren zur Zuordnung anonymer Kassenbons zu vorgegebenen Kundengruppen entwickelt, um Ergebnisse von Kundensimulationen auf die Gesamtheit der anonymen Kunden eines Unternehmens übertragen zu können. Nutzen und Qualität der entwickelten Modelle, Verfahren und Maße werden mit Hilfe einer umfangreichen Software-Implementierung anhand mehrerer Anwendungsbeispiele aus der Praxis demonstriert und in einigen Fallstudien evaluiert — basierend auf realen Daten eines deutschen Einzelhandelsunternehmens.The focus of this work is the development of an agent-based, probabilistic model for representing and analysing individual consumer behaviour. The model provides a basis for decision making in marketing and especially in customer relationship management (CRM). As foundation of the model, a class of probabilistic agents is introduced. These agents can be merged to holonic agents (holons) and have probabilistic knowledge bases adapted from Bayesian networks (behaviour networks). An individual customer is modelled as a customer agent which is a probabilistic holon consisting of several feature agents. A feature agent represents a particular property (feature) of the customer';s behaviour and encapsulates appropriate feature-related behaviour networks. The total behaviour of a customer agent is determined by interaction of its feature agents. Individual behaviour patterns of a customer are extracted from real data — in consideration of given domain knowledge — and are represented within behaviour networks as non-linear dependencies between influencing factors and the customer';s product-related reactions. Behaviour simulation is realised by evaluation of expected reactions of customers on given shopping scenarios based on quantifiable, probabilistic reasoning. Customer agents are able to join to customer group agents which represent different behaviour aggregations of their members. Based on behaviour networks, several behaviour-related methods of analysis as well as distance measures are developed to identify homogeneous customers on the basis of their dynamic shopping behaviour. Subsequently, existing vector-based methods of classification and clustering are extended by these behaviour-related methods and measures. In addition, methods are developed to assign anonymous receipts to given customer groups in order to extent customer-related simulation results to anonymous customers of a company. Benefits and quality of the developed models, methods and measures, which are implemented within a complex software system, are shown by practical examples and evaluated in several case studies — based on real data from a German retailer

Maschinelles Lernen Bayes'scher Netze in benutzeradaptiven Systemen

Das Thema der vorliegenden Arbeit ist die Anwendung existierender sowie die Entwicklung neuer, spezifisch auf den Fall benutzeradaptiver Systeme zugeschnittener, maschineller Lernverfahren für Bayes'sche Netze. Bislang werden die in benutzeradaptiven Systemen eingesetzten Bayes'schen Netze meist manuell—anhand von theoretischen Überlegungen (von Experten)—konstruiert. Es bietet sich an, die im System anfallenden Interaktionsdaten im Rahmen des Konstruktions- bzw. Wartungsprozesses durch die Anwendung entsprechender maschineller Lernverfahren zur Verbesserung der Systemperformanz auszunutzen. Dieser Arbeit liegt eine integrative Konzeption des maschinellen Lernens Bayes';scher Netze für benutzeradaptive Systeme zugrunde, die gemäß den Anforderungen der zu modellierenden Domäne mit alternativen Verfahren instanziiert werden kann. In diesem Rahmen werden in dieser Arbeit neu entwickelte maschinelle Lern- bzw. Adaptionsverfahren für Bayes';sche Netze vorgestellt, die das gemeinsame Ziel verfolgen, die besonderen Eigenschaften und Anforderungen des Benutzermodellierungskontexts während des Lern- bzw. Adaptionsvorgangs zu berücksichtigen. Diese neuen Verfahren werden in vergleichenden Studien mit alternativ einsetzbaren existierenden Methoden des maschinellen Lernens Bayes'scher Netze evaluiertThis thesis focuses on the application of existing and the development of new Bayesian network learning methods that are able to deal with or that can exploit the characteristics of domains of user-adaptive systems. So far, Bayesian networks used by user-adaptive systems have typically been specified manually—on the basis of theoretical considerations (of experts). It seems to be a promising approach to exploit the interaction data that can be collected during the systems'; use through the application of machine learning methods in the design and maintenance phases. We present an integrative generic framework that can be instantiated with alternative methods according to the demands of the domain to be modeled. To this end, new Bayesian network learning and adaptation methods are presented that jointly aim to address adequately the characteristics and demands of the user modeling context during the learning and adaptation processes. These methods are evaluated in comparative empirical studies relative to alternative existing standard Bayesian network learning procedures

Enhancing maritime defence and security through persistently autonomous operations and situation awareness systems

This thesis is concerned with autonomous operations with Autonomous Underwater Vehicles(AUVs) and maritime situation awareness in the context of enhancing maritime defence and security. The problem of autonomous operations with AUVs is one of persistence. That is, AUVs get stuck due to a lack of cognitive ability to deal with a situation and require intervention from a human operator. This thesis focuses on addressing vehicle subsystem failures and changes in high level mission priorities in a manner that preserves autonomy during Mine Counter measures (MCM) operations in unknown environments. This is not a trivial task. The approach followed utilizes ontologies for representing knowledge about the operational environment, the vehicle as well as mission planning and execution. Reasoning about the vehicle capabilities and consequently the actions it can execute is continuous and occurs in real time. Vehicle component faults are incorporated into the reasoning process as a means of driving adaptive planning and execution. Adaptive planning is based on a Planning Domain Definition Language (PDDL) planner. Adaptive execution is prioritized over adaptive planning as mission planning can be very demanding in terms of computational resources. Changes in high level mission priorities are also addressed as part of the adaptive planning behaviour of the system. The main contribution of this thesis regarding persistently autonomous operations is an ontological framework that drives an adaptive behaviour for increasing persistent autonomy of AUVs in unexpected situations. That is, when vehicle component faults threaten to put the mission at risk and changes in high level mission priorities should be incorporated as part of decision making. Building maritime situation awareness for maritime security is a very difficult task. High volumes of information gathered from various sources as well as their efficient fusion taking into consideration any contradictions and the requirement for reliable decision making and (re)action under potentially multiple interpretations of a situation are the most prominent challenges. To address those challenges and help alleviate the burden from humans which usually undertake such tasks, this thesis is concerned with maritime situation awareness built with Markov Logic Networks(MLNs) that support humans in their decision making. However, commonly maritime situation awareness systems rely on human experts to transfer their knowledge into the system before it can be deployed. In that respect, a promising alternative for training MLNs with data is presented. In addition, an in depth evaluation of their performance is provided during which the significance of interpreting an unfolding situation in context is demonstrated. To the best of the author’s knowledge, it is the first time that MLNs are trained with data and evaluated using cross validation in the context of building maritime situation awareness for maritime security

Constructing situation specific belief networks

This paper describes a process for constructing situation-specific belief networks from a knowledge base of network fragments. A situation-specific network is a minimal querycomplete network constructed from a knowledge base in response to a query for the probability distribution on a set of target variables given evidence and context variables. We present definitions of query completeness and situation-specific networks. We describe conditions on the knowledge base that guarantee query completeness. The relationship of our work to earlier work on KBMC is also discussed.