Realization of the Cognitive Architecture LIDA in the Building Automation Domain
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Cognitive Architectures and Autonomy: a Comparative Review
Journal of Artificial General Intelligence 3(2) 1-30, 2012 Submitted 2012-02-17 DOI: 10.2478/v10229-011-0015-3 Accepted 2012-05-11 Cognitive Architectures and Autonomy: A Comparative Review Kristinn R. Th órisson 1,2 THORISSON@ RU. IS Helgi Páll Helgasson 1 HELGIH09@ RU. IS 1Center for Analysis & Design of Intelligent Agents / School of Computer Science Venus, 2nd fl. Reykjavik University Menntavegur 1, 101 Reykjavik, Iceland 2Icelandic Institute for Intelligent Machines 2. h. Uranus Menntavegur 1, 101 Reykjavik, Iceland Editor: Wlodzislaw Duch Abstract One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. -
Agent Architecture: an Overview
TRANSACTIONS ON SCIENCE AND TECHNOLOGY 2014. Vol. 1, No 1, pp 18-35 DECEMBER 2014 /18 Agent Architecture: An Overview Kim On CHIN1*, Kim Soon GAN2, Rayner ALFRED3, Patricia ANTHONY4 & Dickson LUKOSE5 1,2,3 Center of Excellence in Semantic Agent, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, MALAYSIA. 4Department of Applied Computing, Faculty of Environment, Society and Design, Lincoln University, Christchurch, NEW ZEALAND. 5Artificial Intelligence Center, MIMOS Berhad, Technology Park Malaysia, Bukit Jalil, Kuala Lumpur, MALAYSIA. *Corresponding author: [email protected], Tel: +6088-320000, Ext: 3086 A b s t r a c t Received: 20 Aug 2014 Agent architecture has been one of the core components in building an Revised: 30 Aug 2014 agent application. Agent architecture is considered as the functional brain Accepted: 2 Sept 2014 of an agent in making decision and reasoning to solve problem and Online: 30 Dec 2014 achieving goals. This paper reviews some of the existing agent architectures such as logic-based architecture, reactive architecture, BDI Keywords: architecture, hybrid architecture, cognitive architecture, and semantic Agent; Multi-Agent; Agent architecture. The purpose of this study is to identify distinctive features of Architecture; Semantic Web the different types of agent architectures and how they are implemented to Technology solve real world problems. Introduction The advancement of the Internet technology has increased the need for distributed, concurrent, heterogeneous and dynamic application systems. Agent technology is a new paradigm suitable for developing such systems that situates and operates in a dynamic and heterogeneous environment. What exactly is an agent? To date, there is no widely accepted definition of what an agent is. -
Generic Cognitive Architecture for Real-Time, Embedded Cognitive
School of Computing Blekinge Institute of Technology Generic Cognitive Architecture for Real- Time, Embedded Cognitive Systems Jekaterina Novikova Thesis submitted for completion of Master of Science (60 credits) Main field of study: Computer Science Specialization: Informatics August 2011 School of Computing Blekinge Institute of Technology SE-371 79 Karlskrona Sweden This thesis is submitted to the School of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science (60 credits) in Computer Science with specialization in Informatics. The thesis is equivalent to 10 weeks of full time studies. Contact Information: Author: Jekaterina Novikova E-mail: [email protected] University advisor: Prof. Craig Lindley School of Computing School of Computing Internet : www.bth.se/com Blekinge Institute of Technology Phone : +46 455 38 50 00 SE-371 41 Karlskrona Fax : + 46 455 38 50 57 Sweden ii ABSTRACT Context. The problem of integrated cognition belongs to a multi-disciplinary area of cognitive engineering. The multi-disciplinary focusing on cognitive models and real-time embedded systems, such as mobile robots, helps to reveal a broader and deeper understanding of robotics as part of everyday life and society. Over the past decades many cognitive architectures have been proposed and steadily developed, based on different approaches and methodologies, but still current cognitive architectures are far from the goal of covering the requirements for general intelligence. Recent research in the area of evolutionary algorithms and genetic programming is used in this study as an inspiration for developing the new version of integrated cognitive architecture, and the knowledge of human brain structure and functions is applied to the architecture as well. -
LIDA: a Systems-Level Architecture for Cognition, Emotion, and Learning Stan Franklin , Tamas Madl, Sidney D’Mello and Javier Snaider
1 LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning Stan Franklin , Tamas Madl, Sidney D’Mello and Javier Snaider that parsimoniously explain data from single tasks will never Abstract —We describe a cognitive architecture (LIDA) that generalize to memory as a whole…” [3]. Cognitive architects affords attention, action selection and human-like learning Langley, Laird and Rogers argue that “Instead of carrying out intended for use in controlling cognitive agents that replicate micro-studies that address only one issue at a time, we should human experiments as well as performing real-world tasks. attempt to unify many findings into a single theoretical LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and framework, then proceed to test and refine that theory” [4]. In multimodal instructionalist and selectionist learning. Empirically line with these views, this paper presents a summary account grounded in cognitive science and cognitive neuroscience, the of our systems-level conceptual LIDA cognitive model LIDA architecture employs a variety of modules and processes, (LIDA C) together with its implemented computational each with its own effective representations and algorithms. LIDA cognitive architecture (LIDA I) as a candidate for the unified has much to say about motivation, emotion, attention, and theoretical framework called for above. Discussing LIDA’s autonomous learning in cognitive agents. In this paper we summarize the LIDA model together with its resulting agent contributions to the open issues in cognitive modeling listed architecture, describe its computational implementation, and by Langley et al, [4], as well as its answers to previously discuss results of simulations that replicate known experimental suggested criteria for models of human cognition [5], we data.