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Intelligent Agents
Intelligent Agents Authors: Dr. Gerhard Weiss, SCCH GmbH, Austria Dr. Lars Braubach, University of Hamburg, Germany Dr. Paolo Giorgini, University of Trento, Italy Outline Abstract ..................................................................................................................2 Key Words ..............................................................................................................2 1 Foundations of Intelligent Agents.......................................................................... 3 2 The Intelligent Agents Perspective on Engineering .............................................. 5 2.1 Key Attributes of Agent-Oriented Engineering .................................................. 5 2.2 Summary and Challenges................................................................................ 8 3 Architectures for Intelligent Agents ...................................................................... 9 3.1 Internal Agent Architectures .......................................................................... 10 3.2 Social Agent Architectures............................................................................. 11 3.3 Summary & Challenges ................................................................................. 12 4 Development Methodologies................................................................................ 13 4.1 Overall Characterization ................................................................................ 13 4.2 Selected AO Methodologies.......................................................................... -
Intelligent Agents - Catholijn M
ARTIFICIAL INTELLIGENCE – Intelligent Agents - Catholijn M. Jonker and Jan Treur INTELLIGENT AGENTS Catholijn M. Jonker and Jan Treur Vrije Universiteit Amsterdam, Department of Artificial Intelligence, Amsterdam, The Netherlands Keywords: Intelligent agent, Website, Electronic Commerce Contents 1. Introduction 2. Agent Notions 2.1 Weak Notion of Agent 2.2 Other Notions of Agent 3. Primitive Agent Concepts 3.1 External primitive concepts 3.2 Internal primitive concepts 3.3. An Example Analysis 4. Business Websites 5. A Generic Multi-Agent Architecture for Intelligent Websites 6. Requirements for the Website Agents 6.1 Characteristics and Requirements for the Website Agents 6.2 Characteristics and Requirements for the Personal Assistants 7. The Internal Design of the Information Broker Agents 7.1 A Generic Information Broker Agent Architecture 7.2 The Website Agent: Internal Design 7.3 The Personal Assistant: Internal Design Glossary Bibliography Biographical Sketches Summary In this article first an introduction to the basic concepts of intelligent agents is presented. The notion of weak agent is briefly described, and a number of external and internal primitive agent concepts are introduced. Next, as an illustration, application of intelligent agents to business Websites is addressed. An agent-based architecture for intelligentUNESCO Websites is introduced. Requirements – EOLSS of the agents that play a role in this architecture are discussed. Moreover, their internal design is discussed. 1. IntroductionSAMPLE CHAPTERS The term agent has become popular, and has been used for a wide variety of applications, ranging from simple batch jobs and simple email filters, to mobile applications, to intelligent assistants, and to large, open, complex, mission critical systems (such as systems for air traffic control). -
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. -
Multi-Agent Reinforcement Learning: an Overview∗
Delft University of Technology Delft Center for Systems and Control Technical report 10-003 Multi-agent reinforcement learning: An overview∗ L. Bus¸oniu, R. Babuska,ˇ and B. De Schutter If you want to cite this report, please use the following reference instead: L. Bus¸oniu, R. Babuska,ˇ and B. De Schutter, “Multi-agent reinforcement learning: An overview,” Chapter 7 in Innovations in Multi-Agent Systems and Applications – 1 (D. Srinivasan and L.C. Jain, eds.), vol. 310 of Studies in Computational Intelligence, Berlin, Germany: Springer, pp. 183–221, 2010. Delft Center for Systems and Control Delft University of Technology Mekelweg 2, 2628 CD Delft The Netherlands phone: +31-15-278.24.73 (secretary) URL: https://www.dcsc.tudelft.nl ∗This report can also be downloaded via https://pub.deschutter.info/abs/10_003.html Multi-Agent Reinforcement Learning: An Overview Lucian Bus¸oniu1, Robert Babuskaˇ 2, and Bart De Schutter3 Abstract Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learn- ing concerns reinforcement learning techniques. This chapter reviews a representa- tive selection of Multi-Agent Reinforcement Learning (MARL) algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competi- tive) tasks. The benefits and challenges of MARL are described. A central challenge in the field is the formal statement of a multi-agent learning goal; this chapter re- views the learning goals proposed in the literature. -
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) . -
An Exploration in Stigmergy-Based Navigation Algorithms
From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms عمر بهر تيری محبت ميری خدمت گر رہی ميں تری خدمت کےقابل جب هوا توچل بسی )اقبال( To my late parents with love and eternal appreciation, whom I lost during my PhD studies Örebro Studies in Technology 79 ALI ABDUL KHALIQ From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms © Ali Abdul Khaliq, 2018 Title: From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms Publisher: Örebro University 2018 www.publications.oru.se Print: Örebro University, Repro 05/2018 ISSN 1650-8580 ISBN 978-91-7529-253-3 Abstract Ali Abdul Khaliq (2018): From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms. Örebro Studies in Technology 79. Navigation is a core functionality of mobile robots. To navigate autonomously, a mobile robot typically relies on internal maps, self-localization, and path plan- ning. Reliable navigation usually comes at the cost of expensive sensors and often requires significant computational overhead. Many insects in nature perform robust, close-to-optimal goal directed naviga- tion without having the luxury of sophisticated sensors, powerful computational resources, or even an internally stored map. They do so by exploiting a simple but powerful principle called stigmergy: they use their environment as an external memory to store, read and share information. In this thesis, we explore the use of stigmergy as an alternative route to realize autonomous navigation in practical robotic systems. In our approach, we realize a stigmergic medium using RFID (Radio Frequency Identification) technology by embedding a grid of read-write RFID tags in the floor. -
Expert Assessment of Stigmergy: a Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. -
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 Agent
INTELLIGENT AGENT INTELLIGENT AGENT By, P.V. Raja Shekar & CH. Madhuri MRITS MCA FINAL YEAR. INTRODUCTION 1 | P a g e INTELLIGENT AGENT 1 What is artificial intelligence? 2 Branches of artificial intelligence. 3 Introduction to intelligence agent. 4 What is an agent. 5 What is a rational agent. 6 Common attributes of intelligent agent 7 What is bounded rationality. 8 What is an environment and different types of them. 9 Different agent architectures. 10 Applications . 11 Limitations . 12 Conclusion . Artificial Intelligence(AI) Artificial intelligence (AI) is both the intelligence of machines and the branch of computer science which aims to create it. Artificial Intelligence is a combination of cognitive science, linguistics, ontology, physiology, psychology, philosophy, operations research, economics, control theory, neuroscience, computer science, probability, optimization and logic. AI is a very 2 | P a g e INTELLIGENT AGENT large subject-matter. It consists of many different fields, from machine vision to expert systems. The aim of all the fields is the creation of machines that can "think". Researches hope that AI machines will be capable of reasoning, knowledge, learning, communication, planning, perception and the ability to move and manipulate objects. What's it all about? Artificial Intelligence (AI) is the science and engineering of creating intelligent machines and computer programs. It is related to similar tasks of using computers to understand human intelligence but AI does not only have to confine itself to methods that are biologically observable. The interest in AI has commonly increased since years and some classical sub- disciplines like robotics, language processing, or natural computing have produced reliable solutions Intelligence is prediction Intelligence is the ability to predict what comes next, even when faced with incomplete knowledge. -
A Systematic Approach to Artificial Agents
A Systematic Approach to Artificial Agents Mark Burgin1 and Gordana Dodig-Crnkovic2 1 Department of Mathematics, University of California, Los Angeles, California, USA, 2 School of Innovation, Design and Engineering, Mälardalen University, Sweden, Abstract. Agents and agent systems are becoming more and more important in the development of a variety of fields such as ubiquitous computing, ambient intelligence, autonomous computing, intelligent systems and intelligent robotics. The need for improvement of our basic knowledge on agents is very essential. We take a systematic approach and present extended classification of artificial agents which can be useful for understanding of what artificial agents are and what they can be in the future. The aim of this classification is to give us insights in what kind of agents can be created and what type of problems demand a specific kind of agents for their solution. Keywords: Artificial agents, classification, perception, reasoning, evaluation, mobility 1 Introduction Agents are advanced tools people use to achieve different goals and to various problems. The main difference between ordinary tools and agents is that agents can function more or less independently from those who delegated agency to the agents. For a long time people used only other people and sometimes animals as their agents. Developments in information processing technology, computers and their networks, have made possible to build and use artificial agents. Now the most popular approach in artificial intelligence is based on agents. Intelligent agents form a basis for many kinds of advanced software systems that incorporate varying methodologies, diverse sources of domain knowledge, and a variety of data types. -
Adversarial Attack and Defense in Reinforcement Learning-From AI Security View Tong Chen , Jiqiang Liu, Yingxiao Xiang, Wenjia Niu*, Endong Tong and Zhen Han
Chen et al. Cybersecurity (2019) 2:11 Cybersecurity https://doi.org/10.1186/s42400-019-0027-x SURVEY Open Access Adversarial attack and defense in reinforcement learning-from AI security view Tong Chen , Jiqiang Liu, Yingxiao Xiang, Wenjia Niu*, Endong Tong and Zhen Han Abstract Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks. Keywords: Reinforcement learning, Artificial intelligence, Security, Adversarial attack, Adversarial example, Defense Introduction best moves with reinforcement learning from games of Artificial intelligence (AI) is providing major break- self-play. Meanwhile, for Atari game, Mnih et al. (2013) throughs in solving the problems that have withstood presented the first deep learning model to learn con- many attempts of natural language understanding, speech trol policies directly from high-dimensional sensory input recognition, image understanding and so on. The latest using reinforcement learning. Moreover, Liang et al. (Guo studies (He et al.