Remote Agent: to boldly go where no AI system has gone before
TL;DR: The Remote Agent is described, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future of space exploration.
About: This article is published in Artificial Intelligence.The article was published on 1998-08-01 and is currently open access. It has received 727 citations till now. The article focuses on the topics: Autonomous agent & Procedural reasoning system.
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01 Jan 1999
TL;DR: This second edition has been completely revised, capturing the tremendous developments in multiagent systems since the first edition appeared in 1999.
Abstract: Multiagent systems are made up of multiple interacting intelligent agents -- computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the frame of their objectives They are the enabling technology for a wide range of advanced applications relying on distributed and parallel processing of data, information, and knowledge relevant in domains ranging from industrial manufacturing to e-commerce to health care This book offers a state-of-the-art introduction to multiagent systems, covering the field in both breadth and depth, and treating both theory and practice It is suitable for classroom use or independent study This second edition has been completely revised, capturing the tremendous developments in multiagent systems since the first edition appeared in 1999 Sixteen of the book's seventeen chapters were written for this edition; all chapters are by leaders in the field, with each author contributing to the broad base of knowledge and experience on which the book rests The book covers basic concepts of computational agency from the perspective of both individual agents and agent organizations; communication among agents; coordination among agents; distributed cognition; development and engineering of multiagent systems; and background knowledge in logics and game theory Each chapter includes references, many illustrations and examples, and exercises of varying degrees of difficulty The chapters and the overall book are designed to be self-contained and understandable without additional material Supplemental resources are available on the book's Web site Contributors:Rafael Bordini, Felix Brandt, Amit Chopra, Vincent Conitzer, Virginia Dignum, Jurgen Dix, Ed Durfee, Edith Elkind, Ulle Endriss, Alessandro Farinelli, Shaheen Fatima, Michael Fisher, Nicholas R Jennings, Kevin Leyton-Brown, Evangelos Markakis, Lin Padgham, Julian Padget, Iyad Rahwan, Talal Rahwan, Alex Rogers, Jordi Sabater-Mir, Yoav Shoham, Munindar P Singh, Kagan Tumer, Karl Tuyls, Wiebe van der Hoek, Laurent Vercouter, Meritxell Vinyals, Michael Winikoff, Michael Wooldridge, Shlomo Zilberstein
1,692 citations
11 Sep 2000
TL;DR: A verification and testing environment for Java, called Java PathFinder (JPF), which integrates model checking, program analysis and testing, and uses state compression to handle big states and partial order and symmetry reduction, slicing, abstraction, and runtime analysis techniques to reduce the state space.
Abstract: The majority of the work carried out in the formal methods community throughout the last three decades has (for good reasons) been devoted to special languages designed to make it easier to experiment with mechanized formal methods such as theorem provers and model checkers. In this paper, we give arguments for why we believe it is time for the formal methods community to shift some of its attention towards the analysis of programs written in modern programming languages. In keeping with this philosophy, we have developed a verification and testing environment for Java, called Java PathFinder (JPF), which integrates model checking, program analysis and testing. Part of this work has consisted of building a new Java Virtual Machine that interprets Java bytecode. JPF uses state compression to handle large states, and partial order reduction, slicing, abstraction and run-time analysis techniques to reduce the state space. JPF has been applied to a real-time avionics operating system developed at Honeywell, illustrating an intricate error, and to a model of a spacecraft controller, illustrating the combination of abstraction, run-time analysis and slicing with model checking.
1,459 citations
Additional excerpts
...This wasfor examplethecasewith the RemoteAgent[32] mentionedabove....
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TL;DR: An introduction to the motivation and concepts of autonomic computing is provided and some research that has been seen as seminal in influencing a large proportion of early work is described, including the works that have provided significant contributions to an established reference model.
Abstract: Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ‘hype’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.
918 citations
Cites background or methods from "Remote Agent: to boldly go where no..."
...In fact, NASA has already previously used autonomic behavior in its DS1 (Deep Space 1) mission and the Mars Pathfinder [Muscettola et al. 1998]....
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...In fact, NASA has already previously used autonomic behavior in its DS1 (Deep Space 1) mission and the Mars Path.nder [Muscettola et al. 1998]....
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...…Rutherford et al. 2002; Candea et al. 2003; Bennani and Menasce 2005; Sharma et al. 2003 Badger 2004; Garlan and Schmerl 2002a Kenyon 2001; Muscettola et al. 1998; Burrell et al. 2004; McCann et al. 2006; Wieselthier et al. 2002 Kephart and Chess 2003; Bantz et al. 2003; Ganek and…...
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TL;DR: The motivations for research on cognitive architectures are examined, some candidates that have been explored in the literature are reviewed, and some properties that a cognitive architecture should exhibit related to representation, organization, performance, and learning are considered.
662 citations
Book•
01 Jan 2004
TL;DR: Developing Intelligent Agent Systems not only answers the questions 'what are agents?' and 'why are they useful?' but also the crucial question: 'how do I design and build intelligent agent systems?'
Abstract: Build your own intelligent agent system. Intelligent agent technology is a tool of modern computer science that can be used to engineer complex computer programmes that behave rationally in dynamic and changing environments. Applications range from small programmes that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This powerful technology is not widely used, however, as developing intelligent agent software requires high levels of training and skill. The authors of this book have developed and tested a methodology and tools for developing intelligent agent systems. With this methodology (Prometheus) developers can start agent-oriented designs and implementations easily from scratch saving valuable time and resources. Developing Intelligent Agent Systems not only answers the questions 'what are agents?' and 'why are they useful?' but also the crucial question: 'how do I design and build intelligent agent systems?' The book covers everything a practitioner needs to know to begin to effectively use this technology - including an introduction to the notion of agents, a description of the concepts involved, and a software engineering methodology.
638 citations
Cites background from "Remote Agent: to boldly go where no..."
...An extreme example of an agent system that was required to deal with such situations was Remote Agent (Muscettola et al. 1998), which, in May 1999, was in control of NASA’s Deep Space 1 for two days, over 96 500 000 kilometres from the Earth....
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References
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Journal Article•
TL;DR: An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation, which is notable in offering a delicate balance between space and time.
Abstract: An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
7,466 citations
"Remote Agent: to boldly go where no..." refers background in this paper
...The above constraint template is closely related to temporally scoped operators used in temporal planning approaches [3]....
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TL;DR: In this paper, an interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation, which is notable in offering a delicate balance between time and space.
Abstract: An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
7,362 citations
01 Jan 1988
TL;DR: Brooks et al. as mentioned in this paper decompose an intelligent system into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much.
Abstract: Brooks, R.A., Intelligence without representation, Artificial Intelligence 47 (1991) 139159. Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments.
4,202 citations
TL;DR: Brooks et al. as discussed by the authors decompose an intelligent system into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much.
3,783 citations
01 Jan 1999
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
Abstract: LNAI was established in the mid-1980s as a topical subseries of LNCS focusing on artificial intelligence. This subseries is devoted to the publication of state-of-the-art research results in artificial intelligence, at a high level and in both printed and electronic versions making use of the well-established LNCS publication machinery. As with the LNCS mother series, proceedings and postproceedings are at the core of LNAI; however, all other sublines are available for LNAI as well. The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
3,464 citations