A Roadmap of Agent Research and Development
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Behavior Trees in Robotics and AI
Michele Colledanchise and Petter Ogren¨ Behavior Trees in Robotics and AI An Introduction arXiv:1709.00084v4 [cs.RO] 3 Jun 2020 Contents 1 What are Behavior Trees? ....................................... 3 1.1 A Short History and Motivation of BTs . .4 1.2 What is wrong with FSMs? The Need for Reactiveness and Modularity . .5 1.3 Classical Formulation of BTs. .6 1.3.1 Execution Example of a BT . .9 1.3.2 Control Flow Nodes with Memory . 11 1.4 Creating a BT for Pac-Man from Scratch . 12 1.5 Creating a BT for a Mobile Manipulator Robot . 14 1.6 Use of BTs in Robotics and AI . 15 1.6.1 BTs in autonomous vehicles . 16 1.6.2 BTs in industrial robotics . 18 1.6.3 BTs in the Amazon Picking Challenge . 20 1.6.4 BTs inside the social robot JIBO . 21 2 How Behavior Trees Generalize and Relate to Earlier Ideas . 23 2.1 Finite State Machines . 23 2.1.1 Advantages and disadvantages . 24 2.2 Hierarchical Finite State Machines . 24 2.2.1 Advantages and disadvantages . 24 2.2.2 Creating a FSM that works like a BTs . 29 2.2.3 Creating a BT that works like a FSM . 32 2.3 Subsumption Architecture . 32 2.3.1 Advantages and disadvantages . 33 2.3.2 How BTs Generalize the Subsumption Architecture . 33 2.4 Teleo-Reactive programs . 33 2.4.1 Advantages and disadvantages . 34 2.4.2 How BTs Generalize Teleo-Reactive Programs . 35 2.5 Decision Trees . 35 2.5.1 Advantages and disadvantages . -
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. -
History of Robotics: Timeline
History of Robotics: Timeline This history of robotics is intertwined with the histories of technology, science and the basic principle of progress. Technology used in computing, electricity, even pneumatics and hydraulics can all be considered a part of the history of robotics. The timeline presented is therefore far from complete. Robotics currently represents one of mankind’s greatest accomplishments and is the single greatest attempt of mankind to produce an artificial, sentient being. It is only in recent years that manufacturers are making robotics increasingly available and attainable to the general public. The focus of this timeline is to provide the reader with a general overview of robotics (with a focus more on mobile robots) and to give an appreciation for the inventors and innovators in this field who have helped robotics to become what it is today. RobotShop Distribution Inc., 2008 www.robotshop.ca www.robotshop.us Greek Times Some historians affirm that Talos, a giant creature written about in ancient greek literature, was a creature (either a man or a bull) made of bronze, given by Zeus to Europa. [6] According to one version of the myths he was created in Sardinia by Hephaestus on Zeus' command, who gave him to the Cretan king Minos. In another version Talos came to Crete with Zeus to watch over his love Europa, and Minos received him as a gift from her. There are suppositions that his name Talos in the old Cretan language meant the "Sun" and that Zeus was known in Crete by the similar name of Zeus Tallaios. -
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. -
Intelligence Without Representation: a Historical Perspective
systems Commentary Intelligence without Representation: A Historical Perspective Anna Jordanous School of Computing, University of Kent, Chatham Maritime, Kent ME4 4AG, UK; [email protected] Received: 30 June 2020; Accepted: 10 September 2020; Published: 15 September 2020 Abstract: This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by the evolutionary development of our own intelligence and that his approach mirrored how our own intelligence functions. Thus, the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact on artificial intelligence and cognitive theory at the time, as well as on modern-day approaches to AI. Keywords: behaviour-based robotics; representation; adaptive behaviour; evolutionary robotics 1. Introduction In 1991, Rodney Brooks published the paper Intelligence without representation, a seminal work on behaviour-based robotics [1]. This paper influenced many aspects of research into artificial intelligence and cognitive theory. In Intelligence without representation, Brooks described his behaviour-based robotics approach to artificial intelligence. He highlighted a number of points that he considers fundamental in modelling intelligence. -
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. -
AAAI-16 / IAAI-16 / EAAI-16 Program
Thirtieth AAAI Conference on Artificial Intelligence Twenty-Eighth Conference on Innovative Applications of Artificial Intelligence The Sixth Symposium on Educational Advances in AI AAAI-16 / IAAI-16 / EAAI-16 Program February 12–17, 2016 Phoenix Convention Center / Hyatt Regency Phoenix Phoenix, Arizona, USA Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by AI Journal, the National Science Foundation, Baidu, IBM Research, Infosys, Lionbridge, Microsoft Research, Disney Research, University of Southern California/Information Sciences Institute, Yahoo Labs!, ACM/SIGAI, CRA Computing Community Consortium (CCC), and David E. Smith MORNING AFTERNOON EVENING (AFTER 5:00 PM) Friday, February 12 Tutorial Forum Tutorial Forum Student Welcome Reception Workshops Workshops AAAI/SIGAI DC AAAI/SIGAI DC Saturday, February 13 Tutorial Forum Tutorial Forum Speed Dating Workshops Workshops Opening Reception AAAI/SIGAI DC AAAI/SIGAI DC EAAI EAAI Open House Invited Talks Open House Robotics Exhibits Robotics Exhibits Sunday, February 14 AAAI / IAAI Welcome / AAAI Awards Lunch with a Fellow Presidential Address: Dietterich IAAI Invited Talk: Rao AAAI Invited Talk: Krause AAAI / IAAI Technical Program AAAI / IAAI Technical Program AI Job Fair Classic Paper/Robotics Talks What’s Hot Talks / Robotics Talks Poster / Demo Session 1 EAAI EAAI Fellows Dinner Robotics/Vendor Exhibits Robotics/Vendor Exhibits Video Competition Viewing Video Competition Viewing Monday, February 15 Women’s Mentoring Breakfast AAAI Invited Talk: -
Origins of the American Association for Artificial Intelligence
AI Magazine Volume 26 Number 4 (2006)(2005) (© AAAI) 25th Anniversary Issue The Origins of the American Association for Artificial Intelligence (AAAI) Raj Reddy ■ This article provides a historical background on how AAAI came into existence. It provides a ratio- nale for why we needed our own society. It pro- vides a list of the founding members of the com- munity that came together to establish AAAI. Starting a new society comes with a whole range of issues and problems: What will it be called? How will it be financed? Who will run the society? What kind of activities will it engage in? and so on. This article provides a brief description of the consider- ations that went into making the final choices. It also provides a description of the historic first AAAI conference and the people that made it happen. The Background and the Context hile the 1950s and 1960s were an ac- tive period for research in AI, there Wwere no organized mechanisms for the members of the community to get together and share ideas and accomplishments. By the early 1960s there were several active research groups in AI, including those at Carnegie Mel- lon University (CMU), the Massachusetts Insti- tute of Technology (MIT), Stanford University, Stanford Research Institute (later SRI Interna- tional), and a little later the University of Southern California Information Sciences Insti- tute (USC-ISI). My own involvement in AI began in 1963, when I joined Stanford as a graduate student working with John McCarthy. After completing my Ph.D. in 1966, I joined the faculty at Stan- ford as an assistant professor and stayed there until 1969 when I left to join Allen Newell and Herb Simon at Carnegie Mellon University Raj Reddy. -
Formalizing Common Sense Reasoning for Scalable Inconsistency-Robust Information Integration Using Direct Logictm Reasoning and the Actor Model
Formalizing common sense reasoning for scalable inconsistency-robust information integration using Direct LogicTM Reasoning and the Actor Model Carl Hewitt. http://carlhewitt.info This paper is dedicated to Alonzo Church, Stanisław Jaśkowski, John McCarthy and Ludwig Wittgenstein. ABSTRACT People use common sense in their interactions with large software systems. This common sense needs to be formalized so that it can be used by computer systems. Unfortunately, previous formalizations have been inadequate. For example, because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic. Our goal is to develop a standard foundation for reasoning in large-scale Internet applications (including sense making for natural language) by addressing the following issues: inconsistency robustness, classical contrapositive inference bug, and direct argumentation. Inconsistency Robust Direct Logic is a minimal fix to Classical Logic without the rule of Classical Proof by Contradiction [i.e., (Ψ├ (¬))├¬Ψ], the addition of which transforms Inconsistency Robust Direct Logic into Classical Logic. Inconsistency Robust Direct Logic makes the following contributions over previous work: Direct Inference Direct Argumentation (argumentation directly expressed) Inconsistency-robust Natural Deduction that doesn’t require artifices such as indices (labels) on propositions or restrictions on reiteration Intuitive inferences hold including the following: . Propositional equivalences (except absorption) including Double Negation and De Morgan . -Elimination (Disjunctive Syllogism), i.e., ¬Φ, (ΦΨ)├T Ψ . Reasoning by disjunctive cases, i.e., (), (├T ), (├T Ω)├T Ω . Contrapositive for implication i.e., Ψ⇒ if and only if ¬⇒¬Ψ . Soundness, i.e., ├T ((├T) ⇒ ) . Inconsistency Robust Proof by Contradiction, i.e., ├T (Ψ⇒ (¬))⇒¬Ψ 1 A fundamental goal of Inconsistency Robust Direct Logic is to effectively reason about large amounts of pervasively inconsistent information using computer information systems. -
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. -
Program Guide
The First AAAI/ACM Conference on AI, Ethics, and Society February 1 – 3, 2018 Hilton New Orleans Riverside New Orleans, Louisiana, 70130 USA Program Guide Organized by AAAI, ACM, and ACM SIGAI Sponsored by Berkeley Existential Risk Initiative, DeepMind Ethics & Society, Future of Life Institute, IBM Research AI, Pricewaterhouse Coopers, Tulane University AIES 2018 Conference Overview Thursday, February Friday, Saturday, 1st February 2nd February 3rd Tulane University Hilton Riverside Hilton Riverside 8:30-9:00 Opening 9:00-10:00 Invited talk, AI: Invited talk, AI Iyad Rahwan and jobs: and Edmond Richard Awad, MIT Freeman, Harvard 10:00-10:15 Coffee Break Coffee Break 10:15-11:15 AI session 1 AI session 3 11:15-12:15 AI session 2 AI session 4 12:15-2:00 Lunch Break Lunch Break 2:00-3:00 AI and law AI and session 1 philosophy session 1 3:00-4:00 AI and law AI and session 2 philosophy session 2 4:00-4:30 Coffee break Coffee Break 4:30-5:30 Invited talk, AI Invited talk, AI and law: and philosophy: Carol Rose, Patrick Lin, ACLU Cal Poly 5:30-6:30 6:00 – Panel 2: Invited talk: Panel 1: Prioritizing The Venerable What will Artificial Ethical Tenzin Intelligence bring? Considerations Priyadarshi, in AI: Who MIT Sets the Standards? 7:00 Reception Conference Closing reception Acknowledgments AAAI and ACM acknowledges and thanks the following individuals for their generous contributions of time and energy in the successful creation and planning of AIES 2018: Program Chairs: AI and jobs: Jason Furman (Harvard University) AI and law: Gary Marchant