ATLANTIS THINKING MACHINES – serie

The book “Enaction, Embodiment, Evolutionary Robotics” proposes how a particular kind of simulation model, i.e. Evolutionary Robotics simulations, can help to solve several problems in Cognitive Science. Examples discussed in the book ranges from motor control, neuroscientific theory, social contingency and time perception. It is argued that methodological minimalism can be a merit, not a shortcoming, even when studying something as complex as the human mind. The book concludes by proposing a new minimalist interdisciplinary framework for the study of perception, combining simulation modeling, experimental methods and accounts of subjective experience. This book endorses an enactive and constructivist view on the human mind, in opposition to the traditional information-processing view. Furthermore, the book discusses and presents the enactive approach, clarifies the assets of this view and how it differs from other proposed alternatives to the computationalist paradigm in AI and Cognitive Science, crucial and missing in the ongoing “embodied turn”. The book also presents new experimental results on a number of topics and points out connections between them. Finally, the book proposes a novel framework for the study of perception that combines a number of methods, including computational modeling, in a previously unseen and promising way.

The work outlines a novel conceptual and theoretical framework for understanding Artificial General Intelligence and based on this framework outlines a practical roadmap for the development of AGI with capability at the human level and ultimately beyond.

The work outlines a detailed blueprint for the creation of an Artificial General Intelligence system with capability at the human level and ultimately beyond, according to the Cog Prime AGI design and the Open Cog software architecture.

The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.

Computational Creativity, Concept Invention, and General Intelligence in their own right all are flourishing research disciplines producing surprising and captivating results that continuously influence and change our view on where the limits of intelligent machines lie, each day pushing the boundaries a bit further. By 2014, all three fields also have left their marks on everyday life – machine-composed music has been performed in concert halls, automated theorem provers are accepted tools in enterprises’ R&D departments, and cognitive architectures are being integrated in pilot assistance systems for next generation airplanes. Still, although the corresponding aims and goals are clearly similar (as are the common methods and approaches), the developments in each of these areas have happened mostly individually within the respective community and without closer relationships to the goings-on in the other two disciplines. In order to overcome this gap and to provide a common platform for interaction and exchange between the different directions, the International Workshops on “Computational Creativity, Concept Invention, and General Intelligence” (C3GI) have been started. At ECAI-2012 and IJCAI-2013, the first and second edition of C3GI each gathered researchers from all three fields, presenting recent developments and results from their research and in dialogue and joint debates bridging the disciplinary boundaries. The chapters contained in this book are based on expanded versions of accepted contributions to the workshops and additional selected contributions by renowned researchers in the relevant fields. Individually, they give an account of the state-of-the-art in their respective area, discussing both, theoretical approaches as well as implemented systems. When taken together and looked at from an integrative perspective, the book in its totality offers a starting point for a (re)integration of Computational Creativity, Concept Invention,and General Intelligence, making visible common lines of work and theoretical underpinnings, and pointing at chances and opportunities arising from the interplay of the three fields.

The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.

This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications.First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic meaning, explore knowledge resources, describe semantic parsers, and compare two main forms of inference: deduction and abduction.In the main part of the book, we propose an integrative knowledge base combining lexical-semantic, ontological, and distributional knowledge. A particular attention is payed to ensuring its consistency. We then design a reasoning procedure able to make use of the large scale knowledge base. We experiment both with a deduction-based NLU system and with an abductive reasoner. For evaluation, we use three different NLU tasks: recognizing textual entailment, semantic role labeling, and interpretation of noun dependencies.