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

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book

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'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