Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

Spiritual Economy: Resources, Labor, and Exchange in Glastonbury and Sedona

abstract: Current data indicates that a growing number of individuals in the English-speaking world are identifying as “spiritual, but not religious” (SBNR). Using ethnographic data collected at two important sites of spiritual pilgrimage and tourism—Glastonbury, England and Sedona, Arizona—this project argues that seekers at these places produce spirituality as much as they consume it. Using the lens of economy, this project examines how seekers conceptualize the (super-) natural resources at these sites, the laborious practices they perform to transform these resources, and the valuation and exchange of the resultant products. In so doing, the project complicates prevailing notions, both among scholars and the public, that contemporary unaffiliated spirituality is predominantly an individualistic consumer process.Dissertation/ThesisDoctoral Dissertation Religious Studies 201

Insights into the structures and dynamics of the pathogen secreted effectors AVR3A11 and tarp through the application of NMR spectroscopy

The lifecycles of obligate pathogenic and parasitic microorganisms depend on a myriad of interactions with their hosts at the molecular level. The class of bacterial proteins directly responsible for these inter-organism interactions have been termed e�ector proteins and can function in either an extracellular or, once secreted into the host cell, an intracellular environment. Primarily through the use of nuclear magnetic resonance spectroscopy (NMR), I have investigated the biophysical properties of two such bacterial e�ector proteins. TARP (translocated actin recruiting protein) is a largely disordered 100 kDa e�ector, common to all chlamydial species, which functions to remodel the host actin cytoskeleton to facilitate the internalisation of the chlamydial cell. Using constructs of TARP comprising an expected actin binding domain, I have shown through NMR chemical shift indexing and 15N relaxation that although the unbound domain is intrinsically disordered a short region, which aligns to other helical actin binding domains, maintains some helical propensity. Furthermore, these residues map to chemical shift variations in the bound state and the Kd for the interaction has also been determined using isothermal titration calorimetry. AVR3a11 is an 8 kDa e�ector from the pepper pathogen Phytophthora capsici that has been shown to inhibit plant programmed cell death. Using a combination of 2D and 3D NMR experiments I have assigned the majority of the backbone and side-chain resonances from the structured regions of AVR3a11. Through the acquisition and analysis of 13C and 15N edited HSQCNOESY spectra I have also calculated a water re�ned, structural ensemble for AVR3a11. Additionally, analysis of the slow (H:D exchange) and fast (T1, T2 and heteronuclear NOE) dynamic regimes, describes AVR3a11 as a relatively tightly folded helical bundle which also exhibits a signi�cant degree of conformational exchange

Railway capacity calculation in emergency using modified fuzzy random optimization methodology

Accurate estimated capacity of the railway section can provide reliable information to railway operators and engineers in decision-making, particularly, in an emergency situation. However, in an emergency, the optimization of capacity of a railway section is usually involved to study, for example, the characteristics of dynamic, fuzziness, randomness, and non-aftereffect properties. This paper presents a proposed capacity calculation method based on the modified fuzzy Markov chain (MFMC). In this method, the capacity of a railway section in an emergency can be expressed by a fuzzy random variable, which remains the randomness of capacity changing according to the impact of emergencies and the fuzziness of the driving behavior and other factors.Acase study of a high-speed line from Beijing to Shanghai is used to show the process of the proposed methods for optimization of section capacity calculation in an emergency

Gravitational intelligent decision-making model at the fuzzy front end with extrinsic idea integration by the K-means algorithm

© 2022 by the authors.If the dynamic fuzziness of the Front End (FE) part of New Product Development (NPD) cannot be treated in a timely manner, fuzziness accumulates over other parts of NPD hence NPD can result in costly mistakes. The authors tried to remedy this strategically critical problem by implementing mainstream theoretical/methodological approaches, but they found inherent weaknesses of each. The purpose of this study is to bring an objective and intelligent decision-making model to FE so as to lessen fuzziness of it. Model quantizes ideas based on pillars, and re-clusters them with every new idea addition thanks to combining non-mainstream approaches like K-means, distance-based algorithm with gravitational theory inspiration, an accumulation of idea and an exponential function. Study showed that fuzziness of FE can be lessened by quantizing, and objectively managing. The founded core reasons of fuzziness can guide practitioners and authors for better understanding and coping with fuzziness of FE; moreover, the model can be used by companies. Introducing an objective and intelligent decision-making model working like a human brain to FE is a unique idea that has not been tried ever before

Gravitational Intelligent Decision-Making Model at the Fuzzy Front End with Extrinsic Idea Integration by the K-Means Algorithm

If the dynamic fuzziness of the Front End (FE) part of New Product Development (NPD) cannot be treated in a timely manner, fuzziness accumulates over other parts of NPD hence NPD can result in costly mistakes. The authors tried to remedy this strategically critical problem by implementing mainstream theoretical/methodological approaches, but they found inherent weaknesses of each. The purpose of this study is to bring an objective and intelligent decision-making model to FE so as to lessen fuzziness of it. Model quantizes ideas based on pillars, and re-clusters them with every new idea addition thanks to combining non-mainstream approaches like K-means, distance-based algorithm with gravitational theory inspiration, an accumulation of idea and an exponential function. Study showed that fuzziness of FE can be lessened by quantizing, and objectively managing. The founded core reasons of fuzziness can guide practitioners and authors for better understanding and coping with fuzziness of FE; moreover, the model can be used by companies. Introducing an objective and intelligent decision-making model working like a human brain to FE is a unique idea that has not been tried ever before