The Ouroboros Model, Proposal for Self-Organizing General Cognition Substantiated
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Island Biogeography and Conservation
increased catecholamine metabolism) in pocampus (20); when administered to Himwich, J~ Pharmacol. Exp. Ther. 180, 531 (1972). the central nervous system (10), the rabbits, 5-hydroxytryptophan results in 7. R. L. Buckingham and M. Radulovaeki, Brain Res. 99,440 (1975). hippocampus could be one of the sites the most marked rate of increase in 5-HT 8. A. K. Sinha, S. Henriksen, W. C. Dement, J. D. of this restoration process. being found in the hippocampus (21). All Barchas, Am. J. ~hYsiol. 224, 381 (1973). 9. B. E. Jones, P. Bobillier, C. Pin, M. Jouvet, The electrical activity of the hip these findings point to the importance of Bf{iin Res. 58,. 157 (1973). pocampus is unusual. While the rest of a serotonergic mechanism in the hip 10. E. L. Hartmann, Int. psychiat. Clin. 7, 308 (1970); W. C. Stem and E. L. Hartmann, Proc. the brain shows a desynchronized EEG pocampus, and a possible role of this Am. Psychol. Assoc. 7, 308 (1971). pattern during attention, learning, and area in SWS. The specific increases in 11. E. Grastyan, K. Lissak, I. Madarasz, H. Don hoffer, Electroencephalogr. Clin. lVeurophysiol. paradoxical sleep, the hippocampus dis the metabolism of 5-HT and the concen 11,409 (1959); W. R. Adey, D. O. Walter, C. E. tration of DA in the hippocampus during Hendrix, Exp. Neurol. 7, 282 (1961); E. N. plays a syrichronized electrical activity Sokolov, Annu. Rev. Physiol. 25,545 (1963); M. of four to seven cycles per second (theta SWS indicate that the hippocampus func Radulovacki and W. R. Adey, Exp. -
Cognitive Computing Systems: Potential and Future
Cognitive Computing Systems: Potential and Future Venkat N Gudivada, Sharath Pankanti, Guna Seetharaman, and Yu Zhang March 2019 1 Characteristics of Cognitive Computing Systems Autonomous systems are self-contained and self-regulated entities which continually evolve them- selves in real-time in response to changes in their environment. Fundamental to this evolution is learning and development. Cognition is the basis for autonomous systems. Human cognition refers to those processes and systems that enable humans to perform both mundane and specialized tasks. Machine cognition refers to similar processes and systems that enable computers perform tasks at a level that rivals human performance. While human cognition employs biological and nat- ural means – brain and mind – for its realization, machine cognition views cognition as a type of computation. Cognitive Computing Systems are autonomous systems and are based on machine cognition. A cognitive system views the mind as a highly parallel information processor, uses vari- ous models for representing information, and employs algorithms for transforming and reasoning with the information. In contrast with conventional software systems, cognitive computing systems effectively deal with ambiguity, and conflicting and missing data. They fuse several sources of multi-modal data and incorporate context into computation. In making a decision or answering a query, they quantify uncertainty, generate multiple hypotheses and supporting evidence, and score hypotheses based on evidence. In other words, cognitive computing systems provide multiple ranked decisions and answers. These systems can elucidate reasoning underlying their decisions/answers. They are stateful, and understand various nuances of communication with humans. They are self-aware, and continuously learn, adapt, and evolve. -
Heraldic Terms
HERALDIC TERMS The following terms, and their definitions, are used in heraldry. Some terms and practices were used in period real-world heraldry only. Some terms and practices are used in modern real-world heraldry only. Other terms and practices are used in SCA heraldry only. Most are used in both real-world and SCA heraldry. All are presented here as an aid to heraldic research and education. A LA CUISSE, A LA QUISE - at the thigh ABAISED, ABAISSÉ, ABASED - a charge or element depicted lower than its normal position ABATEMENTS - marks of disgrace placed on the shield of an offender of the law. There are extreme few records of such being employed, and then only noted in rolls. (As who would display their device if it had an abatement on it?) ABISME - a minor charge in the center of the shield drawn smaller than usual ABOUTÉ - end to end ABOVE - an ambiguous term which should be avoided in blazon. Generally, two charges one of which is above the other on the field can be blazoned better as "in pale an X and a Y" or "an A and in chief a B". See atop, ensigned. ABYSS - a minor charge in the center of the shield drawn smaller than usual ACCOLLÉ - (1) two shields side-by-side, sometimes united by their bottom tips overlapping or being connected to each other by their sides; (2) an animal with a crown, collar or other item around its neck; (3) keys, weapons or other implements placed saltirewise behind the shield in a heraldic display. -
Order GASTEROSTEIFORMES PEGASIDAE Eurypegasus Draconis
click for previous page 2262 Bony Fishes Order GASTEROSTEIFORMES PEGASIDAE Seamoths (seadragons) by T.W. Pietsch and W.A. Palsson iagnostic characters: Small fishes (to 18 cm total length); body depressed, completely encased in Dfused dermal plates; tail encircled by 8 to 14 laterally articulating, or fused, bony rings. Nasal bones elongate, fused, forming a rostrum; mouth inferior. Gill opening restricted to a small hole on dorsolat- eral surface behind head. Spinous dorsal fin absent; soft dorsal and anal fins each with 5 rays, placed posteriorly on body. Caudal fin with 8 unbranched rays. Pectoral fins large, wing-like, inserted horizon- tally, composed of 9 to 19 unbranched, soft or spinous-soft rays; pectoral-fin rays interconnected by broad, transparent membranes. Pelvic fins thoracic, tentacle-like,withI spine and 2 or 3 unbranched soft rays. Colour: in life highly variable, apparently capable of rapid colour change to match substrata; head and body light to dark brown, olive-brown, reddish brown, or almost black, with dorsal and lateral surfaces usually darker than ventral surface; dorsal and lateral body surface often with fine, dark brown reticulations or mottled lines, sometimes with irregular white or yellow blotches; tail rings often encircled with dark brown bands; pectoral fins with broad white outer margin and small brown spots forming irregular, longitudinal bands; unpaired fins with small brown spots in irregular rows. dorsal view lateral view Habitat, biology, and fisheries: Benthic, found on sand, gravel, shell-rubble, or muddy bottoms. Collected incidentally by seine, trawl, dredge, or shrimp nets; postlarvae have been taken at surface lights at night. -
A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks
sensors Article A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks John Oyekan 1, Windo Hutabarat 1, Christopher Turner 2,* , Ashutosh Tiwari 1, Hongmei He 3 and Raymon Gompelman 4 1 Department of Automatic Control and Systems Engineering, University of Sheffield, Portobello Street, Sheffield S1 3JD, UK; j.oyekan@sheffield.ac.uk (J.O.); w.hutabarat@sheffield.ac.uk (W.H.); a.tiwari@sheffield.ac.uk (A.T.) 2 Surrey Business School, University of Surrey, Guildford, Surrey GU2 7XH, UK 3 School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK; [email protected] 4 IDirect UK Ltd., Derwent House, University Way, Cranfield, Bedfordshire MK43 0AZ, UK; [email protected] * Correspondence: [email protected] Abstract: Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous prolif- Citation: Oyekan, J.; Hutabarat, W.; eration of Internet of Things type devices. To overcome these challenges, this research proposes a Turner, C.; Tiwari, A.; He, H.; knowledge-based cognitive architecture supported by machine learning algorithms for monitoring Gompelman, R. A Knowledge-Based satellite network traffic. The architecture is capable of supporting and augmenting infrastructure Cognitive Architecture Supported by Machine Learning Algorithms for engineers in finding and understanding the causes of faults in network through the fusion of the Interpretable Monitoring of results of machine learning models and rules derived from human domain experience. -
Reinforcement Learning for Adaptive Theory of Mind in the Sigma Cognitive Architecture
Reinforcement Learning for Adaptive Theory of Mind in the Sigma Cognitive Architecture David V. Pynadath1, Paul S. Rosenbloom1;2, and Stacy C. Marsella3 1 Institute for Creative Technologies 2 Department of Computer Science University of Southern California, Los Angeles CA, USA 3 Northeastern University, Boston MA, USA Abstract. One of the most common applications of human intelligence is so- cial interaction, where people must make effective decisions despite uncertainty about the potential behavior of others around them. Reinforcement learning (RL) provides one method for agents to acquire knowledge about such interactions. We investigate different methods of multiagent reinforcement learning within the Sigma cognitive architecture. We leverage Sigma’s architectural mechanism for gradient descent to realize four different approaches to multiagent learning: (1) with no explicit model of the other agent, (2) with a model of the other agent as following an unknown stationary policy, (3) with prior knowledge of the other agent’s possible reward functions, and (4) through inverse reinforcement learn- ing (IRL) of the other agent’s reward function. While the first three variations re-create existing approaches from the literature, the fourth represents a novel combination of RL and IRL for social decision-making. We show how all four styles of adaptive Theory of Mind are realized through Sigma’s same gradient descent algorithm, and we illustrate their behavior within an abstract negotiation task. 1 Introduction Human intelligence faces the daily challenge of interacting with other people. To make effective decisions, people must form beliefs about others and generate expectations about the behavior of others to inform their own behavior. -
ÆTHELMEARC Adeliz Argenti. Badge. Per Saltire Azure and Or, A
ACCEPTANCES Page 1 of 26 February 2007 LoAR THE FOLLOWING ITEMS HAVE BEEN REGISTERED: ÆTHELMEARC Adeliz Argenti. Badge. Per saltire azure and Or, a bordure gules. Aíbell Shúlglas. Badge. Azure, in pale the letter "S" and two bars wavy argent. Artemius of Hunters Home. Holding name and device (see RETURNS for name). Per pale sable and vert, on a plate a leaf vert. Submitted under the name Artemius Le Chaenier. Catrijn van der Hedde. Name. Ceridwen verch y gof. Name and device. Argent, a lion’s head erased contourny vert. Ceridwen is an SCA-compatible Welsh name. Cristina inghean Ghriogair. Name. The submitter requested an authentic Irish Gaelic 13th-15th C name; this is a fine Irish Gaelic name for that period. Cynwyl MacDaire. Name change from Cynwyl MacDaire of Land’s End and badge. Argent, two piles in point sable, each charged with a plate. His old name, Cynwyl MacDaire of Land’s End, is released. Dafydd MacNab. Badge. Vert, a wall issuant from base argent masoned sable with a wooden door proper and on a chief argent three cups azure. We note that in terms of conflict checking, this is equivalent to a field per fess embattled vert and argent masoned sable. Dagr snæbj{o,}rn Bjarnarson. Name and device. Azure, a cross argent goutty gules between four demi-bears couped argent. Edward of Freeholt. Name and device. Vert, a double-bitted axe and on a chief embattled Or an arrow sable. Submitted as Edward of Freehold, there was some question whether Freehold was a reasonable English placename. -
Neural Cognitive Architectures for Never-Ending Learning
Neural Cognitive Architectures for Never-Ending Learning Author Committee Emmanouil Antonios Platanios Tom Mitchelly www.platanios.org Eric Horvitzz [email protected] Rich Caruanaz Graham Neubigy Abstract Allen Newell argued that the human mind functions as a single system and proposed the notion of a unified theory of cognition (UTC). Most existing work on UTCs has focused on symbolic approaches, such as the Soar architecture (Laird, 2012) and the ACT-R (Anderson et al., 2004) system. However, such approaches limit a system’s ability to perceive information of arbitrary modalities, require a significant amount of human input, and are restrictive in terms of the learning mechanisms they support (supervised learning, semi-supervised learning, reinforcement learning, etc.). For this reason, researchers in machine learning have recently shifted their focus towards subsymbolic processing with methods such as deep learning. Deep learning systems have become a standard for solving prediction problems in multiple application areas including computer vision, natural language processing, and robotics. However, many real-world problems require integrating multiple, distinct modalities of information (e.g., image, audio, language, etc.) in ways that machine learning models cannot currently handle well. Moreover, most deep learning approaches are not able to utilize information learned from solving one problem to directly help in solving another. They are also not capable of never-ending learning, failing on problems that are dynamic, ever-changing, and not fixed a priori, which is true of problems in the real world due to the dynamicity of nature. In this thesis, we aim to bridge the gap between UTCs, deep learning, and never-ending learning. -
Connectionist Models of Cognition Michael S. C. Thomas and James L
Connectionist models of cognition Michael S. C. Thomas and James L. McClelland 1 1. Introduction In this chapter, we review computer models of cognition that have focused on the use of neural networks. These architectures were inspired by research into how computation works in the brain and subsequent work has produced models of cognition with a distinctive flavor. Processing is characterized by patterns of activation across simple processing units connected together into complex networks. Knowledge is stored in the strength of the connections between units. It is for this reason that this approach to understanding cognition has gained the name of connectionism. 2. Background Over the last twenty years, connectionist modeling has formed an influential approach to the computational study of cognition. It is distinguished by its appeal to principles of neural computation to inspire the primitives that are included in its cognitive level models. Also known as artificial neural network (ANN) or parallel distributed processing (PDP) models, connectionism has been applied to a diverse range of cognitive abilities, including models of memory, attention, perception, action, language, concept formation, and reasoning (see, e.g., Houghton, 2005). While many of these models seek to capture adult function, connectionism places an emphasis on learning internal representations. This has led to an increasing focus on developmental phenomena and the origins of knowledge. Although, at its heart, connectionism comprises a set of computational formalisms, -
Tomcat: Theory of Mind-Based Cognitive Architecture for Teams
ToMCAT: Theory of Mind-based Cognitive Architecture for Teams September 25, 2019 1 Bay of Pigs invasion (April 1961) Cuban missile crisis (October 1962) Planned by the same team! The difference? Team dynamics. Source: Tetlock & Gardner, Superforecasting (2015) 2 What happens when one or more of the team members are machines? 3 Outline ● Architecture/overview & mission design (Adarsh) ● Hierarchical planning (Clay) ● Dialogue system (Mihai) ● Probabilistic Modeling (Kobus/Emily) ● fNIRS (Kobus/Emily) 4 ToMCAT Architecture Local environment Human teammate 5 Webcam sensor integration 6 Mission design criteria ● Induces emotion (must not be boring!) ● Natural language processing and dialogue can play a significant role. ● Requires hierarchical planning and decision making (inherent in Minecraft’s crafting system) ● Supports interesting multiplayer interactions (competition/cooperation) ● Supports perturbations 7 Starter mission: Search & Rescue with Crafting Context: Creepers have attacked the village, and the survivors are holed up in rooms in their houses, having blockaded the doors against the creepers. But they haven’t got long... Goal: Rescue villagers from creeper-infested village, within some time limit. Features ● Induces emotion (time limit, creepers are dangerous) ● Hierarchical planning (need to craft a weapon to defend against creepers) ● Plan monitoring (need to monitor player health) ● Natural language dialogue (the player can get hints from the agent) ● Natural extension to multiplayer mission. 8 Starter mission: Search & Rescue -
MODELS of COGNITION: the CLASSICAL/CONNECTIONIST DEBATE Suzanne E. Mccalden Submitted in Partial Fulfillrnent of the Requirernen
MODELS OF COGNITION: THE CLASSICAL/CONNECTIONIST DEBATE Suzanne E. McCalden Submitted in partial fulfillrnent of the requirernents for the degree of Masters of Arts Dalhousie University Halifax, Nova Scotia August, 2000 @ Copyright by Suzanne E. McCalden National Library Bibliothèque nationale I*l of Canada du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395, nie Wellingtm Ottawa ON K1A ON4 UnawaON K1AW Canada Canada The author has granted a non- L'auteur a accordé une licence non exclusive licence dowing the exclwive pemettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or seii reproduire, prêter, distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic fonnats. la forme de microfiche/film, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts firom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent êeimprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. For David, Richard, Gordon, Douglas, and especially Evelyn . TABLE OF CONTENTS Table of Contents v Acknowledgements vii Introduction 1 Chapter One: The Classical Theory of Cognition 4 Chapter Two: Comectionisrn and the Systematicity 34 Challenge Chapter Three: Connectionism v. The Language of 69 Thought Hypothesis Bibliography ABSTRACT Fodorts the language of thought hypothesis (LOT) and Smolensky's connectionism are examined. The systematicity debate is also examined. -
A Comparison Among Cognitive Architectures: a Theoretical Analysis
Procedia Computer Science Volume 71, 2015, Pages 56–61 2015 Annual International Conference on Biologically Inspired Cognitive Architectures A Comparison Among Cognitive Architectures: A Theoretical Analysis Danilo Fernando Lucentini1 and Ricardo Ribeiro Gudwin1 University of Campinas (UNICAMP), Campinas, S˜aoPaulo, Brazil [email protected], [email protected] Abstract In this paper we present a theoretical comparison among three of the most popular cogni- tive architectures: SOAR, LIDA and CLARION. These architectures are compared based on a set of cognitive functions supposed to exist in the human cognitive cycle, and how each ar- chitecture deals with them. The comparison emphasizes similarities and differences among the architectures, with the purpose to advise a potential user how to identify the best architecture to employ, depending on the situation. Keywords: Cognitive architectures, Cognitive science, SOAR, CLARION, LIDA 1 Introduction The study of cognitive architectures is a very popular research field in recent years, in the community of intelligent systems. A large body of cognitive architectures has been proposed in the literature (see [2] for a comprehensive table). Unfortunately, most of them are only barely described, without enough details to allow their computational implementation or a freely available source code, which might make them useful for general adoption. A cognitive architecture is basically a computational model that aims to describe, as accurately as possible, the underlying infrastructure for an intelligent system. Thus, the use of cognitive architectures facilitates substantially the effort expended to build artificial agents, by proposing specific models to the construction of such cognitive mechanisms. The focus of this paper is to compare three of the most popular cognitive architectures, which have a long tradition of development in their research groups and (our most strict criteria) pro- vide freely available computational frameworks, besides documentation, for the general public to use.