Maintaining Structured Experiences for Robots via Human Demonstrations: An Architecture To Convey Long-Term Robot\u2019s Beliefs

This PhD thesis presents an architecture for structuring experiences, learned through demonstrations, in a robot memory. To test our architecture, we consider a specific application where a robot learns how objects are spatially arranged in a tabletop scenario. We use this application as a mean to present a few software development guidelines for building architecture for similar scenarios, where a robot is able to interact with a user through a qualitative shared knowledge stored in its memory. In particular, the thesis proposes a novel technique for deploying ontologies in a robotic architecture based on semantic interfaces. To better support those interfaces, it also presents general-purpose tools especially designed for an iterative development process, which is suitable for Human-Robot Interaction scenarios. We considered ourselves at the beginning of the first iteration of the design process, and our objective was to build a flexible architecture through which evaluate different heuristic during further development iterations. Our architecture is based on a novel algorithm performing a oneshot structured learning based on logic formalism. We used a fuzzy ontology for dealing with uncertain environments, and we integrated the algorithm in the architecture based on a specific semantic interface. The algorithm is used for building experience graphs encoded in the robot\u2019s memory that can be used for recognising and associating situations after a knowledge bootstrapping phase. During this phase, a user is supposed to teach and supervise the beliefs of the robot through multimodal, not physical, interactions. We used the algorithm to implement a cognitive like memory involving the encoding, storing, retrieving, consolidating, and forgetting behaviours, and we showed that our flexible design pattern could be used for building architectures where contextualised memories are managed with different purposes, i.e. they contains representation of the same experience encoded with different semantics. The proposed architecture has the main purposes of generating and maintaining knowledge in memory, but it can be directly interfaced with perceiving and acting components if they provide, or require, symbolical knowledge. With the purposes of showing the type of data considered as inputs and outputs in our tests, this thesis also presents components to evaluate point clouds, engage dialogues, perform late data fusion and simulate the search of a target position. Nevertheless, our design pattern is not meant to be coupled only with those components, which indeed have a large room of improvement

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Reasoning about complex agent knowledge - Ontologies, Uncertainty, rules and beyond

Ph.DDOCTOR OF PHILOSOPH

A general cognitive framework for context-aware systems: extensions and applications for high level information fusion approaches

Mención Internacional en el título de doctorContext-aware systems aims at the development of computational systems that process data acquired from different datasources and adapt their behaviour in order to provide the 'right' information, at the 'right' time, in the 'right' place, in the 'right' way to the 'right' person (Fischer, 2012). Traditionally computational research has tried to answer these needs by means of low-level algorithms. In the last years the combination of numeric and symbolic approaches has offered the opportunity to create systems to deal with these issues. However, although the performance of algorithms and the quality of the data directly provided by computers and devices has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This dissertation proposes a set of extensions and applications focused on a cognitive framework for the implementation of context-aware systems based on a general model inspired by the Information Fusion paradigm. This model is stepped in several abstraction levels from low-level raw data to high level scene interpretation whose structure is determined by a set of ontologies. Each ontology level provides a skeleton that includes general concepts and relations to describe entities and their connections. This structure has been designed to promote extensibility and modularity, and might be refined to apply this model in specific domains. This framework combines a priori context knowledge represented with ontologies with real data coming from sensors to support logic-based high-level interpretation of the current situation and to automatically generate feedback recommendations to adjust data acquisition procedures. This work advocates for the introduction of general purpose cognitive layers in order to obtain a closer representation to the human cognition, generate additional knowledge and improve the high-level interpretation. Extensibility and adaptability of the basic ontology levels is demonstrated with the introduction of these traverse semantic layers which are able to be present and represent information at several granularity levels of knowledge using a common formalism. Context-based system must be able to reason about uncertainty. However the reasoning associated to ontologies has been limited to classical description logic mechanisms. This research also tackle the problem of reasoning under uncertainty circumstances through a logic-based paradigm for abductive reasoning: the Belief-Argumentation System. The main contribution of this dissertation is the adaptation of the general architecture and the theoretical proposals to several context-aware application areas such as Ambient Intelligence, Social Signal Processing and surveillance systems. The implementation of prototypes and examples for these areas are explained along this dissertation to progressively illustrate the improvements and extensions in the framework. To initially depict the general model, its components and the basic reasoning mechanisms a video-based Ambient Intelligence application is presented. The advantages and features of the framework extensions through traverse cognitive layers are demonstrated in a Social Signal Processing case for the elaboration of automatic market researches. Finally, the functioning of the system under uncertainty circumstances is illustrated with several examples to support decision makers in the detection of potential threats in common harbor scenarios.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Manuel Molina López.- Secretario: Ángel Arroyo.- Vocal: Nayat Sánchez P

Topics of Thought

This book concerns mental states such as thinking that Obama is tall, imagining that there will be a climate change catastrophe, knowing that one is not a brain in a vat, or believing that Martina Navratilova is the greatest tennis player ever. Such states are usually understood as having intentionality, that is, as being about things or situations to which the mind is directed. The contents of such states are often taken to be propositions. The book presents a new framework for the logic of thought, so understood—an answer to the question: Given that one thinks (believes, knows, etc.) something, what else must one think (ditto) as a matter of logic? This should depend on the propositions which make for the contents of the relevant thoughts. And the book defends the idea that propositions should be individuated hyperintensionally, i.e. not just by the sets of worlds at which they are true (as in standard ‘intensional’ possible worlds semantics), but also by what they are about: their topic or subject matter. Thus, the logic of thought should be ‘topic-sensitive’. After the philosophical foundations have been presented in Chapters 1−2, Chapter 3 develops a theory of Topic-Sensitive Intentional Modals (TSIMs): modal operators representing attitude ascriptions, which embed a topicality or subject matter constraint. Subsequent chapters explore applications ranging from mainstream epistemology (dogmatism, scepticism, fallibilism: Chapter 4), to the nature of suppositional thinking and imagination (Chapter 5), conditional belief and belief revision (Chapter 6), framing effects (Chapter 7), probabilities and indicative conditionals (Chapter 8)

Kiel Declarative Programming Days 2013

This report contains the papers presented at the Kiel Declarative Programming Days 2013, held in Kiel (Germany) during September 11-13, 2013. The Kiel Declarative Programming Days 2013 unified the following events: * 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013) * 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013) * 27th Workshop on Logic Programming (WLP 2013) All these events are centered around declarative programming, an advanced paradigm for the modeling and solving of complex problems. These specification and implementation methods attracted increasing attention over the last decades, e.g., in the domains of databases and natural language processing, for modeling and processing combinatorial problems, and for high-level programming of complex, in particular, knowledge-based systems

On the Topic of Impossibility: a question-sensitive impossible worlds approach to logical omniscience

Desde que Hintikka (1962) propôs uma lógica epistémica modal com base na semântica de mundos possíveis que rapidamente se colocou o problema da omnisciência lógica, isto é, de que a lógica em questão implica que os agentes sabem tudo o que se segue logicamente do que sabem. O próprio Hintikka (1975) tentou resolver o problema introduzindo “mundos possíveis impossíveis”. Desde então, os mundos impossíveis têm sido aplicados no tratamento de várias outras questões filosóficas. Berto e Jago (2019) desenvolvem e exploram várias delas. A presente dissertação começa com uma avaliação em detalhe de soluções para o problema da omnisciência lógica que aceitam mundos impossíveis. De forma a melhor considerar esta perspetiva, questões sobre a caracterização da natureza dos mundos impossíveis e de como representam são consideradas. Por outro lado, filósofos como Yalcin (2018) propõem dar resposta ao problema da omnisciência lógica sem acrescentar mundos impossíveis, mas sim tendo por base uma extensão da noção do conteúdo de frases e estados mentais. De acordo com esta segunda família de perspetivas, o conteúdo de uma frase não é dado simplesmente em termos de condições de verdade, mas também em termos daquilo sobre aquilo que a frase versa. O conhecimento de agentes seria fechado sob implicação lógica que não adiciona nenhum novo assunto ao das proposições que o agente sabe, mas não sob implicação lógica simpliciter. Esta segunda família de perspetivas será igualmente considerada, começando pela questão de o que são assuntos, e terminando por considerar se as várias perspetivas disponíveis conseguem dar conta de todas as diferenças entre conteúdos face aos quais agentes podem ter atitudes proposicionais distintas. Finalmente, será desenvolvida uma solução para o problema da omnisciência lógica que aceita tanto mundos impossíveis, como uma relativização a questões ou assuntos.Ever since Hintikka (1962) proposed an epistemic logic based on possible worlds semantics, the problem of logical omniscience, that is, that the logic proposed by Hintikka would have as a consequence that agents know all the logical consequences of what they know, has been posed as a challenge. Hintikka (1975) himself tried to meet the challenge by introducing “impossible possible worlds”. Since then, impossible worlds have been applied to the treatment of various philosophical questions. Berto and Jago (2019) develop and explore several of them. The present dissertation starts by considering in detail solutions for it that accept impossible worlds. In order to more fully consider this family of perspectives, questions regarding the nature of impossible worlds and how they represent are discussed. On the other hand, philosophers like Yalcin (2018) propose to give a solution to the problem of logical omniscience without adding impossible worlds to a standard possible worlds framework, but rather accepting an extension of the notion of the content of statements and mental states. According to this second family of perspectives, the content of a statement is not simply given in terms of truth-conditions, but also in terms of what the statement is about. Agents’ knowledge would be closed entailment that does not add any new subject matter to the subject matter of what the agent knows, not under logical consequence simpliciter. This second family of perspectives will also be considered, starting from the question of what subject matters and including others, such as whether various perspectives on offer are able to account for all the distinctions between contents to which agents might have different propositional attitudes. Finally, a solution for the problem of logical omniscience that accepts both impossible worlds and subject matters will be developed