Artificial Intelligence Vs (General) Artificial Intelligence 3
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Current Advances in Cognitive Robotics Amir Aly, Sascha Griffiths, Francesca Stramandinoli
Towards Intelligent Social Robots: Current Advances in Cognitive Robotics Amir Aly, Sascha Griffiths, Francesca Stramandinoli To cite this version: Amir Aly, Sascha Griffiths, Francesca Stramandinoli. Towards Intelligent Social Robots: Current Advances in Cognitive Robotics . France. 2015. hal-01673866 HAL Id: hal-01673866 https://hal.archives-ouvertes.fr/hal-01673866 Submitted on 1 Jan 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Proceedings of the Full Day Workshop Towards Intelligent Social Robots: Current Advances in Cognitive Robotics in Conjunction with Humanoids 2015 South Korea November 3, 2015 Amir Aly1, Sascha Griffiths2, Francesca Stramandinoli3 1- ENSTA ParisTech – France 2- Queen Mary University – England 3- Italian Institute of Technology – Italy Towards Emerging Multimodal Cognitive Representations from Neural Self-Organization German I. Parisi, Cornelius Weber and Stefan Wermter Knowledge Technology Institute, Department of Informatics University of Hamburg, Germany fparisi,weber,[email protected] http://www.informatik.uni-hamburg.de/WTM/ Abstract—The integration of multisensory information plays a processing of a huge amount of visual information to learn crucial role in autonomous robotics. In this work, we investigate inherent spatiotemporal dependencies in the data. To tackle how robust multimodal representations can naturally develop in this issue, learning-based mechanisms have been typically used a self-organized manner from co-occurring multisensory inputs. -
Artificial Development of Neural-Symbolic Networks
University of Exeter Department of Computer Science Artificial Development of Neural-Symbolic Networks Joseph Paul Townsend March 2014 Supervised by Dr. Ed Keedwell and Dr. Antony Galton Submitted by Joseph Paul Townsend, to the University of Exeter as a thesis for the degree of Doctor of Philosophy in Computer Science , March 2014. This thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identi- fied and that no material has previously been submitted and approved for the award of a degree by this or any other University. (signature) ................................................................................................. 1 Abstract Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resis- tant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are in- terpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic In- tegration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes. -
Psychological Aspect of Cognitive Robotics
VI Psychological Aspect of Cognitive Robotics 169 CHAPTER 9 Robotic Action Control: On the Crossroads of Cognitive Psychology and Cognitive Robotics Roy de Kleijn Leiden Institute for Brain and Cognition, Leiden University, The Netherlands. George Kachergis Leiden Institute for Brain and Cognition, Leiden University, The Netherlands. Bernhard Hommel Leiden Institute for Brain and Cognition, Leiden University, The Netherlands. CONTENTS 9.1 Early history of the fields .................................... 172 9.1.1 History of cognitive psychology .................... 172 9.1.2 The computer analogy ............................... 173 9.1.3 Early cognitive robots ................................ 174 9.2 Action control ................................................. 174 9.2.1 Introduction ........................................... 175 9.2.2 Feedforward and feedback control in humans ..... 175 9.2.3 Feedforward and feedback control in robots ....... 176 9.2.4 Robotic action planning .............................. 177 9.3 Acquisition of action control ................................. 178 9.3.1 Introduction ........................................... 178 9.3.2 Human action-effect learning ....................... 179 171 172 Cognitive Robotics 9.3.2.1 Traditional action-effect learning research .................................. 179 9.3.2.2 Motor babbling .......................... 179 9.3.3 Robotic action-effect learning ........................ 180 9.4 Directions for the future ...................................... 181 9.4.1 Introduction .......................................... -
A Novel Generative Encoding for Evolving Modular, Regular and Scalable Networks
A Novel Generative Encoding for Evolving Modular, Regular and Scalable Networks Marcin Suchorzewski Jeff Clune Artificial Intelligence Laboratory Creative Machines Laboratory West Pomeranian University of Technology Cornell University [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we introduce the Developmental Symbolic En- Networks designed by humans and evolution have certain coding (DSE), a new generative encoding for evolving net- properties that are thought to enhance both their perfor- works (e.g. neural or boolean). DSE combines elements of mance and adaptability, such as modularity, regularity, hier- two powerful generative encodings, Cellular Encoding and archy, scalability, and being scale-free [12, 11, 1]. In this pa- HyperNEAT, in order to evolve networks that are modular, per we introduce a new generative representation called the regular, scale-free, and scalable. Generating networks with Developmental Symbolic Encoding that evolves networks these properties is important because they can enhance per- that possess these properties. We first review the mean- formance and evolvability. We test DSE’s ability to gener- ing of these terms before discussing them with respect to ate scale-free and modular networks by explicitly rewarding previous evolutionary algorithms. these properties and seeing whether evolution can produce Modularity is the encapsulation of function within mostly networks that possess them. We compare the networks DSE independently operating units, where the internal workings evolves to those of HyperNEAT. The results show that both of the module tend to only affect the rest of the structure encodings can produce scale-free networks, although DSE through the module’s inputs and outputs [12, 11]. -
Artificial Biochemical Networks : Evolving Dynamical Systems to Control Dynamical Systems
This is a repository copy of Artificial Biochemical Networks : Evolving Dynamical Systems to Control Dynamical Systems. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/75277/ Version: Accepted Version Article: Lones, Michael Adam, Fuente, Luis Alberto, Turner, Alexander Phillip et al. (4 more authors) (2014) Artificial Biochemical Networks : Evolving Dynamical Systems to Control Dynamical Systems. IEEE Transactions on Evolutionary Computation. 6423886. pp. 145- 166. ISSN 1089-778X https://doi.org/10.1109/tevc.2013.2243732 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, PREPRINT, ACCEPTED FOR PUBLICATION DECEMBER 2012 1 Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems Michael A. Lones, Senior Member, IEEE, Luis A. Fuente, Alexander P. Turner, Leo S. D. Caves, Susan Stepney, Stephen L. Smith, Member, IEEE, and Andy M. Tyrrell, Senior Member, IEEE Abstract—Biological organisms exist within environments in different evolutionary algorithms have been used to design which complex, non-linear dynamics are ubiquitous. -
Cognitive Robotics
Volume title 1 The editors c 2006 Elsevier All rights reserved Chapter 24 Cognitive Robotics Hector Levesque and Gerhard Lakemeyer This chapter is dedicated to the memory of Ray Reiter. It is also an overview of cognitive robotics, as we understand it to have been envisaged by him.1 Of course, nobody can control the use of a term or the direction of research. We apologize in advance to those who feel that other approaches to cognitive robotics and related problems are inadequately represented here. 24.1 Introduction In its most general form, we take cognitive robotics to be the study of the knowledge rep- resentation and reasoning problems faced by an autonomous robot (or agent) in a dynamic and incompletely known world. To quote from a manifesto by Levesque and Reiter [42]: “Central to this effort is to develop an understanding of the relationship between the knowledge, the perception, and the action of such a robot. The sorts of questions we want to be able to answer are • to execute a program, what information does a robot need to have at the outset vs. the information that it can acquire en route by perceptual means? • what does the robot need to know about its environment vs. what need only be known by the designer? • when should a robot use perception to find out if something is true as opposed to reasoning about what it knows was true in the past? • when should the inner workings of an action be available to the robot for reasoning and when should the action be considered primitive or atomic? 1To the best of our knowledge, the term was first used publicly by Reiter at his lecture on receiving the IJCAI Award for Research Excellence in 1993. -
Artificial Intelligence and Broadband Divide
Artificial Intelligence and Broadband Divide State of ict Connectivity in Asia and the Pacific 2017 The Economic and Social Commission for Asia and the Pacific (ESCAP) serves as the United Nations’ regional hub promoting cooperation among countries to achieve inclusive and sustainable development. The largest regional intergovernmental platform with 53 Member States and 9 associate members, ESCAP has emerged as a strong regional think-tank offering countries sound analytical products that shed insight into the evolving economic, social and environmental dynamics of the region. The Commission’s strategic focus is to deliver on the 2030 Agenda for Sustainable Development, which it does by reinforcing and deepening regional cooperation and integration to advance connectivity, financial cooperation and market integration. ESCAP’s research and analysis coupled with its policy advisory services, capacity building and technical assistance to governments aims to support countries’ sustainable and inclusive development ambitions. The shaded areas of the map indicate ESCAP members and associate members. Information and statistics presented in this publication include only those member and associate member States located in the Asia-Pacific region. Disclaimer: This report of the Information and Communications Technology and Disaster Risk Reduction Division provides policy-relevant analysis on regional trends and challenges in support of the development of the Asia-Pacific Information Superhighway and inclusive development. The views expressed herein are those of the authors, and do not necessarily reflect the views of the United Nations. This report has been issued without formal editing, and the designations employed and material presented do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
Cognitive Robotics
Cognitive Robotics . Cognitive Robotics . Embodied AI/Embodied CS . Robot capable of perception, reasoning, learning, deliberation, planning, acting, interacting, etc. Cognitive Architecture . Unified Theory of Cognition . Cognitive Models Cognitive Systems and Robotics • Since 2001: Cognitive Systems intensely funded by the EU "Robots need to be more robust, context-aware and easy-to-use. Endowing them with advanced learning, cognitive and reasoning capabilities will help them adapt to changing situations, and to carry out tasks intelligently with people" Research Areas . Biorobotics . Bio-inspirata, biomimetica, etc. Enactive Robotics . Dynamic interaction with the environment icub . Developmental Robotics (Epigenetic) . Robot learns as a baby . Incremental sensorimotor and cognitive ability [Piaget] . Neuro-Robotics . Models from cognitive neuroscience . Prosthesis, wearable systems, BCI, etc. Cog Developmental Robotics . iCub Platform . Italian Institute of Technology . EU project RobotCub: open source cognitive humanoid platf. Developmental Robotics . iCub Platform . Italian Institute of Technology . EU project RobotCub: open source cognitive humanoid platf. Developmental Robotics . iCub Platform Developmental Robotics . iCub Platform Developmental Robotics . Cognitive Architecture: The procedural memory is a network of associations between action events and perception events When an image is presented to the memory, if a previously stored image matches the stored image is recalled; otherwise, the presented image is stored Affordance . Affordance: property of an object or a feature of the immediate environment, that indicates how to interface with that object or feature ("action possibilities" latent in the environment [Gibson 1966]) . “The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or ill. The verb to afford is found in the dictionary, but the noun affordance is not. -
Report of Comest on Robotics Ethics
SHS/YES/COMEST-10/17/2 REV. Paris, 14 September 2017 Original: English REPORT OF COMEST ON ROBOTICS ETHICS Within the framework of its work programme for 2016-2017, COMEST decided to address the topic of robotics ethics building on its previous reflection on ethical issues related to modern robotics, as well as the ethics of nanotechnologies and converging technologies. At the 9th (Ordinary) Session of COMEST in September 2015, the Commission established a Working Group to develop an initial reflection on this topic. The COMEST Working Group met in Paris in May 2016 to define the structure and content of a preliminary draft report, which was discussed during the 9th Extraordinary Session of COMEST in September 2016. At that session, the content of the preliminary draft report was further refined and expanded, and the Working Group continued its work through email exchanges. The COMEST Working Group then met in Quebec in March 2017 to further develop its text. A revised text in the form of a draft report was submitted to COMEST and the IBC in June 2017 for comments. The draft report was then revised based on the comments received. The final draft of the report was further discussed and revised during the 10th (Ordinary) Session of COMEST, and was adopted by the Commission on 14 September 2017. This document does not pretend to be exhaustive and does not necessarily represent the views of the Member States of UNESCO. – 2 – REPORT OF COMEST ON ROBOTICS ETHICS EXECUTIVE SUMMARY I. INTRODUCTION II. WHAT IS A ROBOT? II.1. The complexity of defining a robot II.2. -
Building Processes Optimization : Toward an Artificial Ontogeny Based Approach
Building processes optimization : Toward an artificial ontogeny based approach Alexandre Devert Directed by Marc Schoenauer and Nicolas Bred`eche Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy June 21, 2009 Ecole Doctorale d’Informatique Universit´eParis-Sud 11 Contents 1 Introduction to Evolutionary Design 5 1.1 Anevolutionaryalgorithmsprimer . 7 1.1.1 Description .......................... 8 1.1.2 Lamarckian and Baldwinian evolutions . 9 1.1.3 Onthe designof anevolutionaryalgorithm . 10 1.2 Simulationandevaluation . 11 1.2.1 Designofanevaluationfunction . 13 1.2.2 Theusageofsimulation . 14 1.3 Representations for Evolutionary Design . .. 15 1.3.1 Blockstacks.......................... 15 1.3.2 Explicit and implicit representations . 18 1.3.3 Modularity .......................... 19 1.3.4 Treestructures .. .. .. .. .. .. .. .. .. .. 21 1.3.5 Grammarsandrewritingsystems . 23 1.3.6 Theevolvabilityissue . 26 1.3.7 Cellularrepresentations . 26 1.4 Preview ................................ 29 2 AguidedtourofEvolutionaryDesign 31 2.1 Explicitrepresentations . 31 2.1.1 Blockstacks.......................... 31 2.1.2 Legostructures.. .. .. .. .. .. .. .. .. .. 32 2.1.3 Antennae ........................... 33 2.1.4 Shapes............................. 34 2.1.5 Robots............................. 35 2.1.6 Tensegritystructures. 36 2.1.7 TopologicalOptimumDesign . 37 2.1.8 Opticalfibersdesign . 38 2.1.9 Bladeshapedesignforwindturbines. 39 2.2 Implicitrepresentations . 40 2.2.1 KarlSims’creatures . 40 2.2.2 Hornby’sL-systems . 41 2.2.3 EnvironmentawareL-systems. 44 2.3 Cellularrepresentations . 45 2.3.1 Virtualcreatures . 46 2.3.2 Skyscraperframes . 47 2.3.3 Trussstructures . .. .. .. .. .. .. .. .. .. 48 2.3.4 Environmentawareness . 50 2.4 Conclusion .............................. 51 1 CONTENTS 3 An Evolutionary algorithm for blueprints 55 3.1 TheBlindBuilderrepresentation . 55 3.1.1 Variationoperators. 57 3.2 Designofsimple3dobjects . -
Applications of Cognitive Robotics in Disassembly of Products
Applications of Cognitive Robotics in Disassembly of Products By Supachai Vongbunyong B. Eng., M. Eng. A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy School of Mechanical and Manufacturing Engineering The University of New South Wales July 2013 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name Vongbunyong First name. Supachai Other name/s: - Abbreviation for degree as g1ven in the University calendar: PhD School· Mechamcal and Manufacturing Engmeering Faculty· Engmeenng Tille. Applications of cogmtlve robotics in disassembly of products Abstract 350 words maximum: (PLEASE TYPE) Disassembly automation has encountered difficulties in disassembly proces11 due to the variability in the planning and operation levels that result from uncertainties in quality-quantity of the products returned. Thus, the concept of cognitive robotics is Implemented to the vision-based and (semi-) destructive disassembly automation for end-of-life electronic products to handle this variability. The system consists of three operating modules, I.e. cognitive robotic module, vision system module, and disassembly operation module. First, the cognitive robotic module controls the system according to its behaviour influenced by four cognitive functions: reasoning, execution monitoring, learning, and revision. The cognitive robotic agent uses rule-based reasoning to schedule the actions according to the existing knowledge and sensed information from the physical world in regard to the disassembly state. Execution monitoring is used to determine accomplishment of the process. The significant Information of the current process is learned and will be Implemented in subsequent processes. Learning also occurs in the form of demonstration conducted by the expert user via the graphic user interface to overcome unresolved complex problem. -
Evolving Gene Expression to Reconfigure Analogue Devices
EVOLVING GENE EXPRESSION TO RECONFIGURE ANALOGUE DEVICES Kester Dean Clegg Thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy. Department of Computer Science May, 2008 Abstract Repeated, morphological functionality, from limbs to leaves, is widespread in nature. Pattern formation in early embryo development has shed light on how and why the same genes are expressed in different locations or at different times. Practitioners working in evolutionary computation have long regarded nature’s reuse of modular functionality with admiration. But repeating na- ture’s trick has proven difficult. To date, no one has managed to evolve the design for a car, a house or a plane. Or indeed anything where the number of interdependent parts exposed to random mutation is large. It seems that while we can use evolutionary algorithms for search-based optimisation with great success, we cannot use them to tackle large, complex designs where functional reuse is essential. This thesis argues that the modular functionality provided by gene reuse could play an important part in evolutionary computation being able to scale, and that by expressing subsets of genes in specific contexts, successive stages of phenotype configuration can be controlled by evolutionary search. We present a conceptual model of context-specific gene expression and show how a ge- nome representation can hold many genes, only a few of which need be ex- pressed in a solution. As genes are expressed in different contexts, their func- tional role in a solution changes. By allowing gene expression to discover phe- notype solutions, evolutionary search can guide itself across multiple search domains.