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We want to build intelligent actors, not just intelligent thinkers. Indeed, it is not even clear how one could assess intelligence in a system that never acted – or, put otherwise, how a system could exhibit intelligence in the absence of action. -Martha Pollack, from Computers and Thought Lecture, IJCAI-91.&&

Authors have agents . . . professional athletes have agents . . . movie stars have agents . . . and you have agents too. Because an agent is someone with expertise who is entrusted to go out and act on your behalf, the computer programs that help you to maximize your computing experiences are called "agents". The next time that you search for specific information on the internet, picture your own agent or group of agents at work, with each knowing just what you're interested in and how important your time is.

Research being conducted by AI scientists is enabling agents to become increasingly sophisticated, independent. . . and helpful!

Best-kept secret agent revealed - No longer just the province of specialist sectors, agent-based computing is changing the way systems interact and how they are managed. By Boris Sedacca. ComputerWeekly.com (October 12, 2006). "Agent-based computing has already transformed processes such as automated financial markets trading, logistics, and industrial robotics. Now it is moving into the mainstream commercial sector as more complex systems with many different components are used by a wider range of businesses. Organisations that have successfully implemented agent technologies include DaimlerChrysler, IBM and the Ministry of Defence. So what are agent technologies? In essence, they are autonomous software systems that can decide for themselves what they need to do. Agents are capable of operating in dynamic and open environments and often interact with other agents - including both people and software. 'Agents are a way to manage interactions between different kinds of computational entities, and to get the right kind of behaviour out of large-scale distributed systems,' says Michael Luck of the School of Electronics and Computer Science at the University of Southampton and executive director of the EU-funded AgentLink [see below] action co-ordination programme."

Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. By Stan Franklin and Art Graesser, Institute for Intelligent Systems, University of Memphis. From 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."

Is There an Intelligent Agent in Your Future? By James A. Hendler. Nature Web Matters (March 11, 1999). This wonderful paper received the AAAI-2000 Effective Expository Writing Award.

Logical Agents. Chapter 7 of the textbook, Artificial Intelligence: A Modern Approach (Second Edition), by Stuart Russell and Peter Norvig. "We begin in Section 7.1 with the overall agent design. Section 7.2 introduces a simple new environment, the wumpus world, and illustrates the operation of a knowledge-based agent without going into any technical detail. Then, in Section 7.3, we explain the general principles of logic. Logic will be the primary vehicle for representing knowledge throughout Part III of the book."

• Also see:
  • Intelligent Agents. Chapter 2 of the textbook, Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. 1995
  • AI on the Web: Intelligent Agents, the resource companion to the textbook with links to reference material, people, research groups, books, companies and much more.
  • Some resources about wumpus world.

Computational Intelligence - A Logical Approach. David Poole, Alan Mackworth and Randy Goebel. Oxford University Press, New York (1998). "The focus is on an intelligent agent acting in an environment. We start with simple agents acting in simple, static environments and gradually increase the power of the agents to cope with more challenging worlds. We make this concrete by repeatedly illustrating the ideas with three different agent tasks: a delivery robot, a diagnostic assistant, and an information slave (the infobot). " - from the Preface.

• Chapter One is also available online and that's where you'll find lots of useful information including an answer to the question "What Is Computational Intelligence?" and this summary of the common features of the aforementioned three agent tasks: "At one level of abstraction, they each have four tasks: Modeling the environment ... Evidential reasoning or perception ... Action ... Learning from past experience .... These tasks cut across all application domains."
• And be sure to check out their CIspace -Tools for Learning Computational Intelligence: "Here are some applets that are designed as tools for learning and exploring concepts in artificial intelligence. They are part of the online resources for Computational Intelligence. If you are teaching or learning about AI, you may use these applets freely." Two of the applets you'll find there are:
  • Robot Control: "A robot is an intelligent agent that perceives, reasons, and acts in time in an environment. It acts to achieve its assigned goals and at the same time avoids getting into undesired states. The robot applet provides a simulation of a robot perceiving and acting under the control of a set of customizable robot controller functions."
  • Planning: "Planning is essential for agents that act in an environment. To solve a goal intelligently, an agent needs to think about what it will do now and in the future. This applet demonstrates planning using the blockworld problem domain and STRIPS representation."

The Many Faces of Agents. By Katia P. Sycara. AI Magazine 19(2): Summer 1998, 11-12. This article introduces the Intelligent Agents special issue of AI Magazine. Articles include: Autonomous Agents as Synthetic Characters by Elliott and Brzezinski (agents that project believable, engaging personae); Constraints and Agents by Eaton, Freuder and Wallace; and Multiagent Systems by Sycara.

UMBC Agent Web. Edited by Tim Finin and Yannis Labrou at the University of Maryland, Baltimore County. Be sure to see Agents 101 and Recommended Papers.

What's An Agent? By Lenny Foner, MIT Media Laboratory. A good, short, non-technical description.

Biological and Social Models of Intelligence: Agents Theories. Section 1.1.4 of Chapter One (available online) of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005). "What are the main themes supporting an agent-oriented and emergent view of intelligence? They include: 1. Agents are autonomous or semi-autonomous. ... 2. Agents are 'situated.' ... 3. Agents are interactional. ... 4. The society of agents is structured. ... 5. Finally, the phenomenon of intelligence in this environment is 'emergent.' ... Based on these observations, we define an agent as an element of a society that can perceive (often limited) aspects of its environment and affect that environment either directly or through cooperation with other agents."

Agents of Cooperation - Orchestrating the actions of mobile snippets of smart software. By Ivars Peterson. Science News (January 2, 1999). "Allowing agents to act on their own is a key requirement. [Pattie] Maes and her colleagues define an autonomous agent as a 'computational system' that can inhabit a complex, constantly changing environment, sense what is going on, and act independently to accomplish a specified set of tasks or achieve certain goals. The underlying software technology is an offshoot of research in artificial intelligence. A software agent often makes inferences on the basis of a set of rules specifying its actions in a variety of situations."

Agent Architecture:

• Agent Architecture. Section 3.5 in The Role of Intelligent Systems in the National Information Infrastructure. The American Association for Artificial Intelligence. Daniel S. Weld, editor. (1995). "Agents, as we defined previously, are entities capable of autonomous goal-oriented behavior in some environment, often in the service of larger-scale goals external to themselves. The architecture of an agent is the computational structure that, along with the more dynamic knowledge represented within it, generates the agent’s behavior in the environment. The architecture must contain structures that enable representing knowledge (Subsection 3.1), representing and achieving goals (Subsection 3.3), interacting with the environment, and coping with unexpected occurrences. Moreover, for many domains, these capabilities must be exhibited in real time. Depending on the nature of the environment, other agents (either human or virtual) in the environment, and the kinds of task the agent should perform in the environment, other capabilities may also need to be supported in the agent’s architecture; for example, coordination and collaboration (Subsection 3.6), language use (Subsection 3.8), learning (Subsection 3.2), and humanlike behavior and affect."
• Intelligent Agent Architecture. Entry by Stanley J. Rosenschein in the MIT Encyclopedia of Cognitive Science. "Intelligent agent architecture is a model of an intelligent information-processing system defining its major subsystems, their functional roles, and the flow of information and control among them. ... An intelligent agent is a device that interacts with its environment in flexible, goal-directed ways, recognizing important states of the environment and acting to achieve desired results. Clearly, when designing a particular agent, many domain-specific features of the environment must be reflected in the detailed design of the agent. Still, the general form of the subsystems underlying intelligent interaction with the environment may carry over from domain to domain. Intelligent agent architectures attempt to capture these general forms and to enforce basic system properties such as soundness of reasoning, efficiency of response, or interruptibility. Many architectures have been proposed that emphasize one or another of these properties, and these architectures can be usefully grouped into three broad categories: the deliberative, the reactive, or the distributed."
• Agent Architectures. An overview of several systems by Patrick Doyle.
• Autonomous Agents as Embodied AI. By Stan Franklin. (1997). Cybernetics and Systems, 28(6): 499-520. "This paper is primarily concerned with answering two questions: What are necessary elements of embodied architectures? How are we to proceed in a science of embodied systems?"
• What sort of architecture is required for a human-like agent? By Aaron Sloman, School of Computer Science & Cognitive Science Research Centre at The University of Birmingham. "A complete functioning agent, whether biological, or simulated in software, or implemented in the form of a robot, needs an integrated collection of diverse but interrelated capabilities, i.e. an architecture."
  • Also see the slides from: Varieties of Evolvable Minds OR How to think about architectures for human-like and other agents OR How to Turn Philosophers of Mind into Engineers - presented in Oxford on January 22, 2001.
  • Also available: the expanded version of the paper.
• Pandemonium, Demons & Oliver Selfridge (see: Namesakes)

AI in the news: Agents

AI and Agents: State of the Art. By Eduardo Alonso. AI Magazine 23(3): Fall 2002, 25-30. "This article is a reflection on agent-based AI. My contention is that AI research should focus on interactive, autonomous systems, that is, agents. Emergent technologies demand so. We see how recent developments in (multi-) agent-oriented research have taken us closer to the original AI goal, namely, to build intelligent systems of general competence. Agents are not the panacea though. I point out several areas such as design description, implementation, reusability, and security that must be developed before agents are universally accepted as the AI of the future."

Electric Elves: Applying Agent Technology to Support Human Organizations. By H. Chalupsky, Y. Gil, C. A. Knoblock, K. Lerman, J. Oh, D. V. Pynadath, T. A. Russ, and M. Tambe. Information Sciences Institute, University of Southern California. To appear in Proceedings of the Thirteenth Annual Conference of Innovative Applications of Artificial Intelligence (IAAI-2001), Seattle, WA, August 2001. Excerpt from the abstract: " The operation of a human organization requires dozens of everyday tasks to ensure coherence in organizational activities, to monitor the status of such activities, to gather information relevant to the organization, to keep everyone in the organization informed, etc. Teams of software agents can aid humans in accomplishing these tasks, facilitating the organization’s coherent functioning and rapid response to crises, while reducing the burden on humans. Based on this vision, this paper reports on Electric Elves, a system that has been operational, 24/7, at our research institute since June 1, 2000."

"Intelligent agent" technology staging a comeback. By Paul Festa. CNET (October 28, 1999). "Consumer confusion about agents is no fluke: The category encompasses such a wide range of products and technologies that even developers disagree over what the term means. Artificial intelligence academics despair over arriving at a universally agreed-upon definition, but among software sellers the term generally is used to denote software that automates certain computing functions and exercises some judgment on the user's behalf."

Towards a Standardization of Multi-Agent System Frameworks. Roberto A. Flores-Mendez. (1999). Crossroads. "It is important that agents not only have ontologies to conceptualize a domain, but also that they have ontologies with similar constructions."

IDA, a Software Agent Cognitive System. By Stan Franklin. ERCIM News (No. 53; April 2003). "This IDA technology is based on a host of disparate mechanisms taken from the 'new' artificial intelligence. These include the Hofstadter and Mitchell's Copycat architecture, Kanerva's sparse distributed memory, Maes' behavior nets, and Jackson's pandemonium theory. IDA is currently up and running, and has been tested to the satisfaction of Navy detailers. Watching IDA in action, their reaction is typically a nod of the head together with 'Yes, that's how I do it.'"

Pattie - MIT professor Pattie Maes has created a stir by making agents a household word.... By Marguerite Holloway. Wired (December 1997; Issue 5.12). "In the past two years, Maes and Firefly have done more for software agents than the semi-intelligent bits of software have ever done for us. Agents - small programs that do electronic tasks for their masters and that can, ideally, learn by watching their user's activities - have been dogged by hype for the past 20 years. The approach that AI researchers had generally used - the deliberative thinking paradigm - had not yielded serviceable autonomous agents, so the promise of the servile bots was never followed by the real thing. Now, however, agents are finding their way into the world in large part because of Maes's pioneering work. Her radical approach flew in the face of traditional knowledge-based AI research."

Dependable Agent Systems. IEEE Intelligent Systems Special Issue (Volume 19, Number 5; September/October 2004). "It is well known that building dependable software systems for dynamic environments is difficult. It is also well known that building large-scale distributed software systems is difficult. The relatively few attempts to combine these two tasks confirm that successfully building large-scale distributed systems with predictable dependability properties is exceptionally difficult. The articles in this special issue of IEEE Intelligent Systems deal with this issue and discuss an emerging and exciting new approach to building these most challenging kinds of systems. " - Abstract: Guest Editors' Introduction.

The Ghost in Your Machine - Computers may soon monitor your work, notice when fatigue sets in, and fix mistakes. BusinessWeek Online Reporter Olga Kharif interviews Chris Forsythe (August 25, 2003). "At their most benign, smart computers seem like executive secretaries for those of us who can't afford one -- offering tremendous advances in productivity. Yet some fear that the concept suggests an ominous encroachment out of a sci-fi movie. Cognitive psychologist Chris Forsythe, who leads the Sandia team, insists that the machines are designed to augment -- not replace -- human activity. 'We don't want to take the human out of the loop,' he says."

Intelligent Software: Programs That Can Act Independently Will Ease the Burdens that Computers Put on People. Patti Maes. (1995). Scientific American 273 (3): 84-86.

A Web Page for Teleo-Reactive Programs. Provided by Nils J. Nilsson, Kumagai Professor of Engineering, Emeritus, Robotics Laboratory, Department of Computer Science, Stanford University. "A teleo-reactive (T-R) program is a mid-level agent control program that robustly directs an agent toward a goal in a manner that continuously takes into account the agent's changing perceptions of a dynamic environment. T-R programs are written in a production-rule-like language and require a specialized interpreter."

Pattie Maes on Software Agents: Humanizing the Global Computer. Interviewed by Charles Petrie and Meredith Wiggins. IEEE Internet Computing Online; Vol. 1, No. 4 (1994). "

Robot Telescopes Comb the Skies. By Lakshmi Sandhana. Wired News (September 21, 2004). "British astronomers have just begun to operate RoboNet-1.0, a global network of the world's biggest robotic telescopes, controlled by intelligent software to effectively act as one giant eye that can be focused anywhere in the sky within a minute. ... ESTAR, a joint project of Liverpool John Moores University and Exeter University, developed intelligent autonomous software programs, known as agents, that will function as the brains of the network. Acting as 'virtual astronomers,' the agents will collect and analyze data 24 hours a day, alerting their flesh-and-blood counterparts only when they catch sight of something noteworthy."

Persistent Assistants: Living and Working with AI: Papers from the 2005 Spring Symposium, ed. Daniel Shapiro, Pauline Berry, John Gersh, Nathan Schurr. Technical Report SS-05-05. American Association for Artificial Intelligence, Menlo Park, California.

• Preface. By Daniel Shapiro, Pauline Berry, John Gersh, and Nathan Schurr. "This document discusses what it will take to enable a future in which intelligent agents play a significant role in personal and professional lives."

Human Responsibility for Autonomous Agents. By Ben Shneiderman. IEEE Intelligent Systems 22(2): March/April 2007, 60-61. Abstract: "Automated or autonomous systems can sometimes fail harmlessly, but they can also destroy data, compromise privacy, and consume resources, such as bandwidth or server capacity. What's more troubling is that automated systems embedded in vital systems can cause financial losses, destruction of property, and loss of life. Controlling these dangers will increase trust while enabling broader use of these systems with higher degrees of safety. Obvious threats stem from design errors and software bugs, but we can't overlook mistaken assumptions by designers, unanticipated actions by humans, and interference from other computerized systems. This article is part of a special issue on Interacting with Autonomy."

Persistent Assistants: Living and Working with AI: Papers from the 2005 Spring Symposium, ed. Daniel Shapiro, Pauline Berry, John Gersh,and Nathan Schurr. Technical Report SS-05-05. American Association for Artificial Intelligence, Menlo Park, California.

Intelligent Agents for Interactive Simulation Environments. By Milind Tambe, W. Lewis Johnson, Randolph M. Jones, Frank Koss, John E. Laird, Paul S. Rosenbloom, and Karl Schwamb. AI Magazine 16(1): Spring 1995, 15-39. Interactive simulation environments are rich domains for investigating intelligent automated agents, with requirements for integration of many agent capabilities but without the costs and demands of perceptual processing or robotic control. This project is aimed at developing humanlike, intelligent agents that can interact with each other, as well as with humans, in such virtual environments.

Privacy-Aware Autonomous Agents for Pervasive Healthcare. By Monica Tentori, Jesus Favela, and Marcela D. Rodríguez. IEEE Intelligent Systems (November/December 2006; 21(6): 55-62. "Pervasive technology in hospital work raises important privacy concerns. Autonomous agents can help developers design privacy-aware systems that handle the threats raised by pervasive technology."

Agents of Change - Autonomous agents are still in the labs but could eventually play a critical role in areas ranging from setting market prices to creating more resilient networks. By Patrick Thibodeau. Computerworld (September 6, 2004). "Over the past year, NASA has been uploading software into the Earth Observing-1 satellite, turning it into a testbed for autonomous agents. The agents -- software programs that are able to learn and can function independently -- are used to manage experiments and operate the spacecraft. The effort is part of a technology initiative that researchers say will reshape IT over the course of many years. Autonomous agents have the potential to become an extraordinarily powerful technology, with the capacity to learn, experiment and act independent of human control. Agents could ultimately improve productivity, increase software reliability and change the operation of markets, particularly supply chains."

Agent-Based Computing in Java. By William Wright. Java Developer's Journal (Vol. 6, Issue 1; January 1, 2001). "Agent-based computing is different from object-oriented computing in several ways. While objects and agents both encapsulate their state, objects have methods that will be executed if invoked by some other object. The object can't decide to offer that service to some objects but not others or to offer it at some times but not others. An agent can decide whether carrying out a request is in line with its goals and choose whether to perform the operation. This is why messages between agents are usually called something like 'requests' rather than 'invocation.'"

AI on the Web: Intelligent Agents, the resource companion to the textbook, Artificial Intelligence: A Modern Approach (Second Edition), by Stuart Russell and Peter Norvig, with links to reference material, people, research groups, books, companies and much more.

"Agent Construction Tools. This page provides a survey of agent construction tools. The tools are categorized as either commercially available products or academic and research projects." From AgentBuilder. Also be sure to see their introduction to agent technology: Why, When, and Where to Use Software Agents.

"AgentLink is a coordinating organisation for research and development activities in the area of agent-based computer systems funded by the European Commission." Resources include:

• Agent Projects
• Agent Software
• AgentLink III Roadmap.
  • Also see this related article: Where now for agent-based computing? IST Results (January 18,2006). "What is the future direction for agent-based systems, one of the most important software R&D areas in recent years? Drawing from a body of some 200 industry and academic organisations, a European project has released a strategic roadmap that hopes to guide evolution of the field over the next decade. AgentLink III, as its name suggests, was the third project in the series and author of the roadmap."
• FAQs such as What is an agent? and Why are agents important?

Autonomous Remote Agent. From NASA's site devoted to the Deep Space 1 spacecraft. "...an artificial intelligence system was placed on board to plan and execute spacecraft activities. In contrast to remote control, this sophisticated set of computer programs acts as an agent of the operations team on board the remote spacecraft. Rather than have humans do the detailed planning necessary to carry out desired tasks, remote agent will formulate its own plans, using high level goals provided by the operations team....Remote agent, like the other high-risk technologies that have now been tested on DS1, promises to make space exploration of the future more productive and more exciting while staying within NASA's limited budget."

Bot Spot: "the spot for all bots." Check out their What's a Bot FAQ and then explore their many bot collections.

CALO: Cognitive Assistant that Learns and Organizes. "SRI International is leading the development of new software that could revolutionize how computers support decision-makers. The Defense Advanced Research Projects Agency (DARPA), under its Perceptive Assistant that Learns (PAL) program, has awarded SRI the first two phases of a five-year contract to develop an enduring personalized cognitive assistant. ... The software, which will learn by interacting with and being advised by its users, will handle a broad range of interrelated decision-making tasks that have in the past been resistant to automation. It will have the capability to engage in and lead routine tasks, and to assist when the unexpected happens. To focus the research on real problems and to ensure the software meets requirements such as privacy, security, and trust, the CALO project researchers will themselves use the technology during its development."

"The Intelligent Software Agents Lab at Carnegie Mellon University's Robotics Institute envisions a world in which autonomous, intelligent software programs, known as software agents, undertake many of the operations performed by human users of the World Wide Web, as well as a multitude of other tasks." - from the Introduction.

Interesting AI Demos and Projects. Maintained by Charles Dyer, University of Wisconsin. Describes projects involving agents in health information systems, web searching, personal shopping, and more.

Knowledge Rich Intelligent Agents at Soar Technology, Inc.: "Achieving human-level reasoning and decision-making for autonomous systems requires agents that are capable of reasoning through large volumes of knowledge. A key element is the ability to resolve conflicts, solve problems, and operate in ambiguous and uncertain situations in the same way as a human expert. To be truly useful, these agents must also be able to interact with humans and other agents in natural ways, communicating in domain-specific languages and explaining their behaviors when required. At Soar Technology, we develop these agents for use in training systems, exploratory experimentation, and for embedded control of unmanned and robotic systems."

TAC, the Trading Agent Competition "is an international forum designed to promote and encourage high quality research into the trading agent problem."

Other References Offline

Agre, P. E. 1995. Computational Research on Interaction and Agency. Artificial Intelligence 72: 1-52.

Bradshaw, Jeffrey, editor. 1997. Software Agents. Cambridge, MA: AAAI Press/MIT Press.

Demazeau, Yves, editor. 1998. Proceedings of the Third International Conference on Multiagent Systems. Menlo Park, CA: AAAI Press.

Ferber, Jacques. 1998. Multi-Agent Systems: Towards a Collective Intelligence. Reading, MA: Addison-Wesley.

Flynn, Julie. 1997. Edinburgh: Where Ersatz Crickets Chirp. Business Week (June 23, 1997): 100.

Huhns, Michael N., and Munindar P. Singh, editors. 1997. Readings in Agents. San Francisco: Morgan Kaufmann.

Lesser, Victor, editor. 1995. Proceedings of the First International Conference on Multiagent Systems. Menlo Park, CA: AAAI Press.

Lyons, Daniel. 1997. The Buzz About Firefly. New York Times Magazine (June 29, 1997): 36-37+.

Maes, Patti. 1994. Agents that Reduce Work and Information Overload. Communications of the ACM, Vol. 37, No. 7.

Maes, Patti., editor. 1990. Designing Autonomous Agents: Theory and Practice From Biology to Engineering and Back. Cambridge, MA: MIT Press.

Nilsson, Nils J. 1998. Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann. Approaches the field of AI by looking at the construction of more and more complex agents.

Pollack, Martha. 1992. The Uses of Plans. Artificial Intelligence 57: 43-68. From the IJCAI-91 Computers and Thought Lecture.

Tokoro, Mario, editor. 1996. Proceedings of the Second International Conference on Multiagent Systems. Topics include coordination, distributed planning, implementing multi-agent systems, market-oriented approaches, multiagent applications, multiagent learning, multiagent search, mutual knowledge, negotiation, organizational aspects, real-world agents, situated agents, sociability, and teams of agents. Menlo Park, CA: AAAI Press.

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