Replicode: A Constructivist Programming Paradigm and Language

Replicode is a language designed to encode short parallel programs and executable models, and is centered on the notions of extensive pattern-matching and dynamic code production. The language is domain independent and has been designed to build systems that are modelbased and model-driven, as production systems that can modify their own code. More over, Replicode supports the distribution of knowledge and computation across clusters of computing nodes. This document describes Replicode and its executive, i.e. the system that executes Replicode constructions. The Replicode executive is meant to run on Linux 64 bits and Windows 7 32/64 bits platforms and interoperate with custom C++ code. The motivations for the Replicode language, the constructivist paradigm it rests on, and the higher-level AI goals targeted by its construction, are described by Thórisson (2012), Nivel and Thórisson (2009), and Thórisson and Nivel (2009a, 2009b). An overview presents the main concepts of the language. Section 3 describes the general structure of Replicode objects and describes pattern matching. Section 4 describes the execution model of Replicode and section 5 describes how computation and knowledge are structured and controlled. Section 6 describes the high-level reasoning facilities offered by the system. Finally, section 7 describes how the computation is distributed over a cluster of computing nodes. Consult Annex 1 for a formal definition of Replicode, Annex 2 for a specification of the executive, Annex 3 for the specification of the executable code format (r-code) and its C++ API, and Annex 4 for the definition of the Replicode Extension C++ API

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

The cost-effectiveness of a new disease management model for frail elderly living in homes for the elderly, design of a cluster randomized controlled clinical trial

<p>Abstract</p> <p>Background</p> <p>The objective of this article is to describe the design of a study to evaluate the clinical and economic effects of a Disease Management model on functional health, quality of care and quality of life of persons living in homes for the elderly.</p> <p>Methods</p> <p>This study concerns a cluster randomized controlled clinical trial among five intervention homes and five usual care homes in the North-West of the Netherlands with a total of over 500 residents. All persons who are not terminally ill, are able to be interviewed and sign informed consent are included. For cognitively impaired persons family proxies will be approached to provide outcome information. The Disease Management Model consists of several elements: (1) Trained staff carries out a multidimensional assessment of the patients functional health and care needs with the interRAI Long Term Care Facilities instrument (LTCF). Computerization of the LTCF produces immediate identification of problem areas and thereby guides individualized care planning. (2) The assessment outcomes are discussed in a Multidisciplinary Meeting (MM) with the nurse, primary care physician, nursing home physician and Psychotherapist and if necessary other members of the care team. The MM presents individualized care plans to manage or treat modifiable disabilities and risk factors. (3) Consultation by an nursing home physician and psychotherapist is offered to the frailest residents at risk for nursing home admission (according to the interRAI LTCF). Outcome measures are Quality of Care indicators (LTCF based), Quality Adjusted Life Years (Euroqol), Functional health (SF12, COOP-WONCA), Disability (GARS), Patients care satisfaction (QUOTE), hospital and nursing home days and mortality, health care utilization and costs.</p> <p>Discussion</p> <p>This design is unique because no earlier studies were performed to evaluate the effects and costs of this Disease Management Model for disabled persons in homes for the elderly on functional health and quality of care.</p> <p>Trail registration number</p> <p>ISRCTN11076857</p

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

Achieving Artificial General Intelligence Through Peewee Granularity

The general intelligence of any autonomous system must in large part be measured by its ability to automatically learn new skills and integrate these with prior skills. Cognitive architectures addressing these topics are few and far between – possibly because of their difficulty. We argue that architectures capable of diverse skill acquisition and integration, and real-time management of these, require an approach of modularization that goes well beyond the current practices, leading to a class of architectures we refer to as peewee-granule systems. The building blocks (modules) in such systems have simple operational semantics and result in architectures that are heterogeneous at the cognitive level but homogeneous at the computational level

Self-Programming: Operationalizing Autonomy

Lacking an operational definition of autonomy has considerably weakened the concept&apos;s impact in systems engineering. Most current “autonomous ” systems are built to operate in conditions more or less fully described a priori, which is insufficient for achieving highly autonomous systems that adapt efficiently to unforeseen situations. In an effort to clarify the nature of autonomy we propose an operational definition of autonomy: a self-programming process. We introduce Ikon Flux, a proto-architecture for self-programming systems and we describe how it meets key requirements for the construction of such systems. Structural Autonomy as Self-Programming We aim at the construction of machines able to adapt to unforeseen situations in open-ended environments