A Perspective on Software Agents Research
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Iot Mag April May 2017.Qxp Layout 1
IoT Now: ISSN 2397-2793 APRIL/MAY 2017 • VOLUME 7 • ISSUE 2 TALKING HEADS Aeris CTO explains why IoT needs and deserves to have security by design INSIDE: The Enterprise Buyer's Guide – Which IoT Platform 2017 SECURE IoT SMART ENERGY INDUSTRIAL IoT TRANSPORT IoT GLOBAL NETWORK How to address the New efficiency for living, The new interconnected Connections for a Log on at growing threat. See our working and playing. manufacturing environment. moving industry. www.iotglobalnetwork.com Analyst Report at See our Analyst Report at See our Analyst Report at Read our exclusive Analyst to discover our new portal for www.iot-now.com www.iot-now.com www.iot-now.com Report inside this issue products, services and insight PLUS: 18 PAGE TRANSPORT INSIGHT REPORT: Unlimit CEO says IoT success in India means focusing on the long tail • HMS Industrial Networks on why it's time to skip the scary stories and focus on what's needed to secure manufacturing IoT • Geolocation in the Port of Barcelona with Actility • AT&T on why IoT platforms must address organisations’ different needs for functionality• Inside Digicel's deployment of Stream Technologies' IoT-X platform with added Starhome Mach technologies in 33 M2M markets • How Vodafone connectivity helps precision manufacturer ensure maximum uptime for machines • News at www.iot-now.com Get to market faster with IoT services 7RƫQGRXWPRUHQRNLDFRPLRW CONTENTS IoT NOW 10 ANALYST 27 TALKING HEADS REPORT 16 50 SECURITY IoT PLATFORMS INTERVIEW TO WATCH IN THIS ISSUE 20 CASE STUDY 58 COMPANY PROFILE Inside Actility, -
Is It an Agent, Or Just a Program?: a Taxonomy for Autonomous Agents
Agent or Program http://www.msci.memphis.edu/~franklin/AgentProg.html Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents Stan Franklin and Art Graesser Institute for Intelligent Systems University of Memphis Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, Springer-Verlag, 1996. Abstract The advent of software agents gave rise to much discussion of just what such an agent is, and of how they differ from programs in general. Here we propose a formal definition of an autonomous agent which clearly distinguishes a software agent from just any program. We also offer the beginnings of a natural kinds taxonomy of autonomous agents, and discuss possibilities for further classification. Finally, we discuss subagents and multiagent systems. Introduction On meeting a friend or colleague that we haven't seen for a while, or a new acquaintance, some version of the following conversation often ensues: What are you working on these days? Control structures for autonomous agents. Autonomous agents? What do you mean by that? A brief explanation is then followed by: But agents sound just like computer programs. How are they different? This elicits a more satisfying explanation that distinguishes between agent and program. The nature of this "more satisfying explanation" motivates this essay. After a review of some of the many ways the term "agent" has been used within the context of autonomous agents, we'll propose and defend a notion of agent that is clearly distinct from a program. This discussion will lead us to a discussion of possible classifications for autonomous agents. -
Extended Resources
Generalization & Democratization of Artificial Intelligence & Machine Learning Extended Resources 1. This TEDxTalk, given by Dr. Ben Goertzel, illustrates some of the most pressing issues of artificial intelligence in today’s world. Dr. Goertzel, CEO of SingularityNET, details the importance of advancing and improving the human experience through a decentralized platform of artificial agents which may ultimately lead to the “singularity” of artificial general intelligence. 2. https://www.youtube.com/watch?v=r4manxX5U-0 3. Fig. 1.1: Agent Interaction. Montes, G. A., & Goertzel, B. (2019). Retrieved October 7, 2020. 4. Fig 1.2: Silo Based Decision Tree Model. Schofield, M., & Thielscher, M., (2019). Retrieved October 15, 2020. 5. Fig 1.3: Conceptual Model For Simulated Recall. Nadji-Tehrani, M., & Eslami, A. (2020). Retrieved October 15, 2020. 6. Fig. 1.4: Basic Neural Network. Zhou, V. (2019, March 3). Retrieved October, 13, 2020. 7. Another TEDxTalk, given by Karen Hao, details the catastrophic effects of artificial intelligence including: deep fakes, job-search discrimination, facial recognition, etc. Hao details the historical success of the improvement in car safety through the adoption and regulation of seatbelts and uses this example to show how the development of AI is outpacing its regulation. Hao proposes that regulation and the evaluation of the technology's impact should happen before the technology is released instead of after. a. https://www.youtube.com/watch?v=D28aL_5LH2Q 8. Podcast conversation discussing artificial general intelligence between Dr. Lex Fridman (MIT) and Dr. Ilya Sutskever (OpenAI). The discussion is around the current AI technologies that have seen advancements toward and possible next steps of technology needed to achieve AGI. -
Usage of Ontologies and Software Agents for Knowledge-Based Design of Mechatronic Systems
INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, ICED’07 28 - 31 AUGUST 2007, CITE DES SCIENCES ET DE L'INDUSTRIE, PARIS, FRANCE USAGE OF ONTOLOGIES AND SOFTWARE AGENTS FOR KNOWLEDGE-BASED DESIGN OF MECHATRONIC SYSTEMS Ewald G. Welp1, Patrick Labenda1 and Christian Bludau2 1Ruhr-University Bochum, Germany 2Behr-Hella Thermocontrol GmbH, Lippstadt, Germany ABSTRACT Already in [1, 2 and 3] the newly developed Semantic Web Service Platform SEMEC (SEMantic and MEChatronics) has been introduced and explained. It forms an interconnection between semantic web, semantic web services and software agents, offering tools and methods for a knowledge-based design of mechatronic systems. Their development is complex and connected to a high information and knowledge need on the part of the engineers involved in it. Most of the tools nowadays available cannot meet this need to an adequate degree and in the demanded quality. The developed platform focuses on the design of mechatronic products supported by Semantic web services under use of the Semantic web as a dynamic and natural language knowledge base. The platform itself can also be deployed for the development of homogenous, i.e. mechanical and electronical systems. Of special scientific interest is the connection to the internet and semantic web, respectively, and its utilization within a development process. The platform can be used to support interdisciplinary design teams at an early phase in the development process by offering context- sensitive knowledge and by this to concretize as well as improve mechatronic concepts [1]. Essential components of this platform are a design environment, a domain ontology mechatronics as well as a software agent. -
A Software System for Agent-Assisted Ontology Building
A SOFTWARE SYSTEM FOR AGENT-ASSISTED ONTOLOGY BUILDING by Denish M umbaiwala B.Eng. Electronics The Maharaja Sayajirao University of Baroda, 2007 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF NORTHERN BRITISH COLUMBIA March 2016 © Denish Mumbaiwala, 2016 Abstract This thesis investigates how one can design a team of intelligent software agents that helps its human partner develop a formal ontology from a relational database and enhance it with higher-level abstractions. The resulting efficiency of ontology devel- opment could facilitate the building of intelligent decision support systems that allow: high-level semantic queries on legacy relational databases; autonomous implementa- tion within a host organization; and incremental deployment without affecting the underlying database or its conventional use. We introduce a set of design principles, formulate the prototype system requirements and architecture, elaborate agent roles and interactions, develop suitable design techniques, and test the approach through practical implementation of selected features. We endow each agent with model meta- ontology, which enables it to reason and communicate about ontology, and planning meta-ontology, which captures the role-specific know-how of the ontology building method. We also assess the maturity of development tools for a larger-scale imple- mentation. Contents Abstract List of Figures vi Acknowledgements viii Dedication 1x 1 Introduction 1 2 Background and Related Work 6 2.1 Decision Support Systems (DSS) . 6 2.1.1 DS Based on Conventional Representation 7 2.1.2 DS Based on Knowledge Representation 8 2.2 The Semantic Web . 9 2.2.1 The Resource Description Framework (RDF) . -
Intelligent Software Agents in Accounting: an Evolving Scenario
Intelligent Software Agents In Accounting: an evolving scenario MIKLOS A. VASARHELYI AND RANI HOITASH Rutgers University Faculty of Management Executive Summary Intelligent agent technology is one of the fastest growing areas of research and Internet related commercial endeavors. It is , however, an ill defined field, with many overstated claims and few specific areas of applications. Both accounting and finance have great potential as fields of application. However, at this stage there are very few, if any, available applications. Most of the applications are still in the primary research stage. For further development of the field, it is necessary to create an operational definition of the field, understand its extant composition, and to postulate a program of research and application development. Such a theoretical work should be of great value as a foundation for an emerging field. Intelligent Agents today claim some functional "intelligence" where they will perform tasks on the behalf of a user. This paper, explores the spectrum of software agency; from the automated "softbots" that are presently being implemented, to the concepts and projects of the future that are more accurately described as intelligent agents. The operational definition section should provide some assessment of the current state of intelligent agent technology and who some of the key players are, to-date. The analysis focuses on academic and commercial research. The paper describes the basic mechanics for agency and how agent developers are tackling the challenge of intelligent agents within networked computing environments. The state-of-the-art section explores the commercial agent landscape. Commercial efforts are just the beginning of the capabilities and potential of intelligent agents. -
Intelligent Agents Web-Mining Agents
Intelligent Agents Web-Mining Agents Prof. Dr. Ralf Möller Dr. Tanya Braun Universität zu Lübeck Institut für Informationssysteme Acknowledgements • Some slides have been designed by PD Dr. Özgür Özcep. Since Özgür works in my group, those slides are not explicitly marked. Thank you nevertheless, Özgür. • Other slides have been take from lecture material provided by researchers on the web. I hope this material is indicated appropriately. Thank you all. 2 Artificial Intelligence and Intelligent Agents • Artificial intelligence (AI) is the science of systematic synthesis and analysis of computational agents that act intelligently – Agents are central to AI (and vice versa) – Intelligent agent = computational agent that acts intelligently – Talking about AI w/o talking about agents misses the point (and vice versa) • Need to technically define the notion of “acting intelligently” • AI = Science of Intelligent Systems – Systems are called computational agents in AI, or agents for short 3 Literature http://aima.cs.berkeley.edu (AIMA, 1st edition 1995) http://artint.info st (AIFCA, 1 edition 2010) 4 Literature 5 Literature 6 What is an Agent? • Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators [AIMA-Def] Sensors Percepts ? Environment Agent Actions – Human agent Actuators eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators – Robotic agent cameras and infrared range finders for sensors; various motors for actuators – Software agent interfaces, data integration, interpretation, data manipulation/output 7 Abstractions: Agents and Environments Sensors Percepts ? Environment Agent Actions Actuators • The agent function maps from percept histories to actions: [f: P* à A] • The agent program runs on Really insist on functional a physical architecture to produce f behavior? • Agent = architecture + program 8 Reactive vs. -
Mitcnc Tech Conference
MIT CLUB OF NORTHERN CALIFORNIA MENS ET MANUS + MACHINES The Future of AI MITCNC TECH CONFERENCE REDWOOD CITY – MARCH , CONFERENCE DETAILS www.mitcnc.org/techconference2017 Discover The Untapped Opportunities of Applied AI AI trailblazers, machine learning experts, forward thinking uncover AI's limitations and untapped opportunities and executives, data scientists, and product and engineering anticipate how AI will change the business landscape. innovators will gather at the MITCNC The Future of AI Conference – Mens Et Manus + Machines – (Mind Don’t miss this business changing opportunity to connect and Hands + Machines) to share how to build with 500 AI founders, execs, and decision makers over an real-world AI solutions as well as discuss the impact of AI action-packed day of unparalleled networking, learning in our society and life. from those who’ve really done it, and of course, great food, generously poured drinks, and plenty of fun. Underneath all the AI hype, real breakthroughs are happening allowing AI developers to create solutions We hope you can join us at the MITCNC The Future of AI which are predictive of the needs of its users. AI and be inspired to make AI work for your business. capabilities are growing at exponential rates. We'd love to see you The MITCNC The Future of AI Conference is where cutting-edge science meets new business implementation. It's a deep dive into emerging AI techniques and technologies with a focus on how to use it in real-world implementations. You'll dissect case studies, delve into the latest -
An Agent-Based Federated Learning Object Search Service
Interdisciplinary Journal of E-Learning and Learning Objects Volume 7, 2011 An Agent-based Federated Learning Object Search Service Carla Fillmann Barcelos and João Carlos Gluz Interdisciplinary Program in Applied Computer Science (PIPCA) – Vale do Rio dos Sinos University (UNISINOS), São Leopoldo, RS, Brasil [email protected]; [email protected] Rosa Maria Vicari Informatics Institute, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brasil [email protected] Abstract The cataloging process represents one of the greatest issues in respect to the use of learning ob- jects because it is through this process that appropriate objects can be found through search en- gines. Incorrect cataloging causes inefficacy in the search process, and this situation is aggravated when the objects are distributed and maintained in several distinct repositories. The present work proposes the creation of an agent-based federated catalog of learning objects: the AgCAT system. This system is part of the MILOS infrastructure, which will provide the computational support to the OBAA metadata proposal, a Brazilian initiative to create a new learning object metadata stan- dard, able to support the requirements of multi-platform adaptability, compatibility with current standards, special needs accessibility, and technological independence of hardware and software platforms. The paper presents the functional structure and organization of the AgCAT system, showing its architecture, the main aspects of its prototype, and main results obtained till now. Keywords: Search Service, Learning Objects, Software Agents. Introduction The Brazilian Ministry of Education provides free digital pedagogical content by means of the Virtual and Interactive Net for Education program (RIVED, 2009), distributing these objects through the International Base of Educational Objects repository (BIOE, 2010). -
Chaos in Innovation and Ai
CHAOS IN INNOVATION AND AI NEXT CHAPTER: THE AGE OF THE BOLD ONES Andreas Sjöström, CTO Digital Advisory Services Your innovation partner Next chapter: The bold ones The bold ones learn fearlessly 4 Children Sleeping Room @ #HACKFORSWEDEN Innovation is uncertainty Most are overweighted in horizon 1 From bell to shark fin: Threat and opportunity Open innovation. Open APIs. Ecosystems. Startups. Where’s the idea? What is it? Does it actually work? Chaos vs order GO Value Ideation Refinement/ Incubation Portfolio of selection future options Chaos vs order Operations Innovation Performance engine Dedicated team Efficient Unregulated Routines & rules Experiments Rational decisions Bold ideas Calculated results Unpredictable outcomes Aimed at guarding the status quo Constantly challenging the status quo Short-term goals Long-term goals Driven by the belief that current Driven by the belief that current operations keep the company alive operations will not fuel growth forever Managing fundamental tensions New types of tests Where to innovate: Design thinking and ecosystems Inspiration Book Airport Onboard Destination Platform eats product for breakfast Book flight Contextual baggage info Flight status Proactive notifications Boarding pass Seat reminder Chatbot @ SAS Biohacking Experimenting, Proof Test Smoke Test, Wow Test Two faces of AI: AI in testing. Testing AI. A human hiding behind the system “Does the system seem to be very reasonable? Does it almost seem like there’s a human hiding behind the system, interacting with me and making me feel comfortable? I get this sense that the system knows a lot about what I want and helps me get the thing that I want. -
The Venue Join Us Hear from Infrastructure Insiders Such As
20 September 2018 | San Francisco, California, USA IEEE Infrastructure Conference Gain insight and inspiration to think, or re-think, your Hear from infrastructure in sessions guided by and in discussion with Infrastructure foremost experts across the technology industry. Insiders such as... Join us as we discuss big picture questions to give you overall, forward thinking insight, as well as operational nuts & bolts for maximizing your infrastructure, such as: Danny Lange | Unity Technologies » Understanding the evolving role of AI to automate, address complex Vice President of AI and Machine challenges, and improve overall productivity and customer experience Learning » Leveraging systems, services design, and operational management in scale Benjamin Treynor Sloss | Google » Managing failures and lessons learned using real-world scenarios Vice President of Engineering and examples » Assessing trust models in your system paradigms » Addressing the engineering culture and customer experience Let's learn from each other, tell our stories, and create a The Venue community and network that we can trust and reach out to when needed. Step away from your daily routine and join us at San What Makes this Conference Different Francisco’s Terra Gallery for the day. The open space encourages » Designed for and by practitioners, with speakers who have all led, managed or built large infrastructures themselves collaboration and networking, while also inspiring innovation. » An intimate, neutral environment for networking and learning with leaders from top technology companies Terra Gallery » Convened by IEEE, the world's largest technical professional association 51 Harrison Street dedicated to advancing technology for the benefit of humanity San Francisco, CA 94105 Topics include: » Artificial Intelligence & Machine Learning » Storage & Data » Micro-services & Programming Models Join Us » Security & Trust » Linux & System Management Registration is open for » Scalability & Reliability 2018 IEEE Infrastructure. -
An Autonomous Software Agent for Navy Personnel Work: a Case Study
From: AAAI Technical Report SS-03-04. Compilation copyright © 2003, AAAI (www.aaai.org). All rights reserved. An Autonomous Software Agent for Navy Personnel Work: a Case Study Stan Franklin Computer Science Department and the Institute for Intelligent Systems University of Memphis Memphis, TN, 38152, USA [email protected] Abstract IDA is currently capable of automating the entire set of IDA is an autonomous software agent whose task is to tasks of a human detailer, with the exception of writing the assign a sailor to a new tour of duty at the end of the old. final orders. The original plan was to add this feature and Her task input is most often initiated by an email from the to eventually deploy an appropriately knowledge sailor; thereafter she acts completely autonomously. The engineered version of IDA for each community. Two years task requires IDA to access databases, to deliberate, to into this five year project, the Navy redirected the project perform complex constraint satisfaction, and to negotiate with the sailor in natural language. IDA’s architecture and toward a multi-agent system in which each of more than mechanisms are motivated by a variety of computational 300,000 sailors and each command would have it own, paradigms and implement a number of cognitive models individual agent. The plan was to complete IDA and then including the Global Workspace Theory of consciousness. to use the IDA technology in the design of the multi-agent IDA is able to interact in depth with sailors in a relatively system. IDA is complete, for these purposes, and a year’s complex real-world environment involving job work on the multi-agent system has produced its first requirements, Navy policy, location, travel, training, dates, demonstrable product.