Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes

Autonomous Acquisition of Natural Language

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora

Ethnic differences in Internal Medicine referrals and diagnosis in the Netherlands

As in other Western countries, the number of immigrants in the Netherlands is growing rapidly. In 1980 non-western immigrants constituted about 3% of the population, in 1990 it was 6% and currently it is more than 10%. Nearly half of the migrant population lives in the four major cities. In the municipality of Rotterdam 34% of the inhabitants are migrants. Health policy is based on the ideal that all inhabitants should have equal access to health care and this requires an efficient planning of health care resources, like staff and required time per patient. The aim of this study is to examine ethnic differences in the use of internal medicine outpatient care, specifically to examine ethnic differences in the reason for referral and diagnosis. Methods We conducted a study with an open cohort design. We registered the ethnicity, sex, age, referral reasons, diagnosis and living area of all ne

Economic evaluation alongside a single RCT of an integrative psychotherapeutic nursing home programme

__Abstract__ There is an 80% prevalence of two or more psychiatric symptoms in psychogeriatric patients. Multiple psychiatric symptoms (MPS) have many negative effects on quality of life of the patient as well as on caregiver burden and competence. Irrespective of the effectiveness of an intervention programme, it is important to take into account its economic aspects

Nivel Corona Cohort: a description of the cohort and methodology used for combining general practice electronic records with patient reported outcomes to study impact of a COVID-19 infection

A population-based COVID-19 cohort was set up in the Netherlands to gain comprehensive insight in the short- and long-term effects of COVID-19 in the general population. A subset of this data, deposited and described here, was used for the aims to describe the methodology and infrastructure used to recruit individuals with COVID-19 and the representativeness of the population-based cohort and to characterize the population by description of their symptoms and health care usage during the acute COVID-19 phase. The starting point of the set-up of the cohort was to recruit participants in routinely recorded, general practice electronic health records (EHR) data, which are sent to the Netherlands Institute for Health Services Research Primary Care Database (Nivel-PCD) on a weekly basis. Patients registered with COVID-19 were flagged in the Nivel-PCD based on their COVID-19 diagnoses. Flagged patients were invited for participation by their general practitioner via a trusted third party. Participating patients received a series of four questionnaires over the duration of one year allowing for a combination of data from patient reported outcomes and EHRs. The Nivel Corona Cohort consists of 442 participants and here a subset of the data from the first questionnaire is shown. The Nivel Corona Cohort is population-based, containing a complete image of severity of symptoms from patients with none or hardly any symptoms to those who were hospitalized due to the COVID-19. The five most prevalent symptoms during the acute COVID-19 phase were fatigue (90.5%), reduced condition (88.2%), coughing/sneezing/stuffy nose (79.3%), headache (75.4%), and myalgia (66.7%). The population-based Nivel Corona Cohort provides ample opportunities for future studies to gain comprehensive insight in the short- and long-term effects of COVID-19 by combining patients’ perspectives and clinical parameters via the EHRs within a long-term follow-up of the cohort

Autocatalytic endogenous reflective architecture

The document describes the architecture of the system being developed in the project. The principal contribution of this work is to the engineering of autonomous systems in a general fashion, i.e. independently of the target domain. This work is not “biologically inspired” and contributing to modeling natural intelligences is not an objective of the project. The main challenge set for the system is to adapt in dynamic open-ended environments with insufficient knowledge and limited resources. The system is to perform in real-time and extract knowledge from the domain it operates in. In particular, this means discovering meaningful states in the environment and learning skills by observing intentional agents in the domain. No system facing the complexity of the real world is able to learn effectively and efficiently from scratch. For each domain our system is plunged in, we hand craft a bootstrap code (called the Masterplan) that consists of the necessary initial and minimal knowledge to observe and act in the domain. The system is model-based and model-driven, meaning that its architecture is unified and consists essentially of dynamic hierarchies of models that capture knowledge in an executable form. The system is thus composed of executable models. From this perspective, learning translates into building models, integrating them into the existing hierarchies and revising them continuously. This perpetual rearranging of the internal agency of the system addresses the first objective of the HUMANOBS project: to design an auto-reconfigurable architecture. Learning is based on model building, which in turn is driven by goals and predictions, i.e. the evaluation by the system of the observed phenomenology of the domain. In other words, the system infers what it shall do (specification) and observes ways to reach these goals (implementation). In that regard, this addresses the second objective of the project: to build a system that can auto-generate specifications for skills and behaviors based on their observation. Learned specifications and implementations are highly context-dependent, which raises the challenge of identifying when to reuse (or refrain from reusing) learned models. Specifications and implementations are built hierarchically, which means that the system is able to reuse previously learned skills, however said skills are by design executable in parallel: this raises the challenge of coordinating (or sequentializing) the operation of the models that implement said skills. The architecture has been designed from the onset to solve these issues, and this addresses the last objective of the project: to build behavior generation and coordination mechanisms for the reproduction and reuse of observed skills. The architecture has been designed in a principled way (each part of the architecture is based on the same architectural template), and organizes the cooperation of four continuous processes: Model Acquisition, Model Revision, Compaction (or model compression) and Reaction (reactive behavior in the domain). It is the interplay of said processes that not only ensure the viability of the whole system but also improves its performance. For example, acquiring models only requires a few examples, performs in real-time and is fast, but this process requires another process that revises models also rapidly and in real-time, and this in turn is supported by the reactive behavior of the system that pays attention only to meaningful entities and phenomena – which it has learned previously, the whole cycle having been bootstrapped by the Masterplan. This is in sharp contrast with traditional Machine Learning approaches which ignore the other cognitive processes of a system and thus, left on their own, require enormous quantities of training examples and cannot perform in real-time. A functional prototype has been developed as a proof of concept of the architecture, and the preliminary results reported in this deliverable strongly indicate that the architecture is sound and that our approach towards the engineering of autonomous systems is tractable and promising. 5/80 This prototype has been expanded, generalized and optimized furthermore into the final state of the architecture (with respect to the time frame of the project). The implemented final system satisfies the requirements of the project (although the evaluation results are presented in a separate deliverable) and, in particular, is completely domain-independent. Future developments and related research avenues have been identified and will bring the architecture beyond the requirements of this project. In the main, these developments are aimed at addressing the issues of (a) adding curiosity and imagination to the system's capabilities and, (b) controlling the autonomous bottom-up growth by means of top-down allonomic constraints

The CareWell-primary care program: design of a cluster controlled trial and process evaluation of a complex intervention targeting community-dwelling frail elderly

Contains fulltext : 109717.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: With increasing age and longevity, the rising number of frail elders with complex and numerous health-related needs demands a coordinated health care delivery system integrating cure, care and welfare. Studies on the effectiveness of such comprehensive chronic care models targeting frail elders show inconclusive results. The CareWell-primary care program is a complex intervention targeting community-dwelling frail elderly people, that aims to prevent functional decline, improve quality of life, and reduce or postpone hospital and nursing home admissions of community dwelling frail elderly. METHODS/DESIGN: The CareWell-primary care study includes a (cost-) effectiveness study and a comprehensive process evaluation. In a one-year pragmatic, cluster controlled trial, six general practices are non-randomly recruited to adopt the CareWell-primary care program and six control practices will deliver 'care as usual'. Each practice includes a random sample of fifty frail elders aged 70 years or above in the cost-effectiveness study. A sample of patients and informal caregivers and all health care professionals participating in the CareWell-primary care program are included in the process evaluation. In the cost-effectiveness study, the primary outcome is the level of functional abilities as measured with the Katz-15 index. Hierarchical mixed-effects regression models / multilevel modeling approach will be used, since the study participants are nested within the general practices. Furthermore, incremental cost-effectiveness ratios will be calculated as costs per QALY gained and as costs weighed against functional abilities. In the process evaluation, mixed methods will be used to provide insight in the implementation degree of the program, patients' and professionals' approval of the program, and the barriers and facilitators to implementation. DISCUSSION: The CareWell-primary care study will provide new insights into the (cost-) effectiveness, feasibility, and barriers and facilitators for implementation of this complex intervention in primary care. TRIAL REGISTRATION: The CareWell-primary care study is registered in the ClinicalTrials.gov Protocol Registration System: NCT01499797