DAI-List Digest Friday, 20 November 1992 Issue Number 98 Topics: Intelligent Agents: Robot Simulator Available Info on Intelligent Agents? Advance Program for Organizational Computing Conference Dissertation Available on the TouringMachine Multiagent Architecture Please send submissions to DAI-List@mcc.com. Send other requests, such as changes in your e-mail address, to DAI-List-Request@mcc.com. ------------------------------------------------------------------------ Date: Wed, 4 Nov 92 09:53:42 PST From: gasser@morue.usc.edu (Les Gasser) Subject: Intelligent Agents: Robot Simulator I found this on comp.ai and thought it might be of interest; maybe someone wants to think about >1 robot? -- Les From: engelson-sean@cs.yale.edu (Sean Philip Engelson) Subject: ARS MAGNA Robot Simulator now available Date: 4 Nov 1992 11:54:17 -0500 Now Available ARS MAGNA The Abstract Robot Simulator Sean P. Engelson Department of Computer Science Yale University ABSTRACT: AI planning research has historically operated in formal abstractions of the real world. This approach was useful in discovering many fundamental issues underlying planning; also, problems in simple domains such as the blocks world can turn out to be surprisingly difficult. Lately attention has turned to planning for more realistic domains in which micro-world simplifying assumptions do not hold. This shift of focus introduces a new problem of validation and comparison of different planning theories and systems. A proper domain for planning problems must be realistically complex but also simple enough to support controlled experimentation. To address these questions, we developed the ARS MAGNA robot simulator. The simulator provides an abstract world in which a planner controls a mobile robot. Mobile robotics is a particularly apposite domain since it is a major application area for AI planning techniques. ARS MAGNA's environment and robot models are based on current robotics research, so that the domain is reasonably realistic. At the same time, we abstracted away from many (though not all) real-world details of kinematics and motor control. Experiments may be controlled by varying global world parameters, such as perceptual noise, as well as building specific environments in order to exercise particular planner features. The world is also extensible to allow new experimental designs that were not thought of originally. The simulator also includes a simple graphical user-interface which uses the CLX interface to the X window system. DOCUMENTATION: Version 1.0 of the ARS MAGNA simulator is documented in Yale Technical Report YALEU/DCS/RR #928, "ARS MAGNA: The Abstract Robot Simulator". This report is available in the distribution as a Postscript(tm) file, as well as from Paula Murano Yale University Department of Computer Science P.O. Box 2158 Yale Station New Haven, CT 06520-2158 Email: murano@cs.yale.edu Any and all comments would be most welcome, and may be directed to me, at engelson@cs.yale.edu. ARS MAGNA can be gotten by anonymous ftp from ftp.cs.yale.edu, as ars-magna.tar.Z in the pub/nisp directory, as follows: % ftp ftp.cs.yale.edu Connected to dept-gw.cs.yale.edu. 220 ra FTP server (SunOS 4.1) ready. Name (ftp.cs.yale.edu:engelson): anonymous 331 Guest login ok, send ident as password. Password: 230 Guest login ok, access restrictions apply. ftp> cd pub/nisp 250 CWD command successful. ftp> bin 200 Type set to I. ftp> get ars-magna.tar.Z 200 PORT command successful. 150 Binary data connection for ars-magna.tar.Z (128.36.17.10,1220) (528589 bytes ). 226 Binary Transfer complete. local: ars-magna.tar.Z remote: ars-magna.tar.Z 528589 bytes received in 7.4 seconds (70 Kbytes/s) ftp> quit 221 Goodbye. % uncompress ars-magna.tar.Z % tar xf ars-magna.tar Installation instructions are in the file Installation.readme. The simulator is written in Nisp, a macro-package for Common Lisp. Nisp can be retrieved in the same way as the simulator. Sean Philip (Shlomo) Engelson Yale Department of Computer Science Box 2158 Yale Station New Haven, CT 06520 ------------------------------------------------------------------------ Subject: Info on intelligent agents? From: Nick Vriend Date: Mon, 12 Oct 92 16:16:48 Nick Vriend European University Institute C.P. 2330 50100 Firenze Ferrovia Italy EARN/Bitnet: As a PhD student of economics at the European University Institute in Florence (Italy), finishing a thesis on 'Decentralized Trade', I am interested in getting contact with people who are working on the following topic: DECENTRALIZED TRADE WITH ARTIFICIALLY INTELLIGENT AGENTS. Basic characteristic of decentralized economies is that each individual agent has a very limited knowledge of his relevant environment. Each agent acts and observes his outcomes in the market (which depend on the actions of the other participants). Thus, each individual agents learns independently, using only a success measure of his own actual performance (e.g., profits, utility). At the moment I am applying Classifier Systems and Genetic Algorithms to model the learning process of each individual agent, but (given the mentioned inherent problem of misspecification in decentralized economies) Neural Networks seem very promising. However, application of Neural Networks appears more complex, as in a decentralized economy nobody would be able to tell each agent what his "target" or "correct" decision would have been. Therefore, the machines have to learn unsupervised (as in e.g., Barto, Sutton & Anderson (1983): Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. IEEE Transactions on Systems, Man, and Cybernetics, 13). Hence, the topic I am interested in might be restated as: REINFORCEMENT LEARNING BY INTERACTING MACHINES. ------------------------------------------------------------------------ Date: Mon, 9 Nov 92 15:09:29 -0600 From: abw@emx.cc.utexas.edu (Andrew B. Whinston) Subject: Organizational Computing Conference -- OC3 (Tentative Program) Fourth Conference on Organizational Computing, Coordination, and Collaboration IC2 Institute University of Texas at Austin Austin, Texas March 28-30, 1993 Sunday, March 28 6:00 - 9:00 PM Reception Monday, March 29 Lynda Applegate: "Chemical Bank Corporaton --- Developing a Communications Infrastructure for the Corporate Systems Division" Harvard Business School Industry Panel Discussion: Discussion of Chemical Bank Case Ken McKenzie: Development of Software Systems for Organizational Design University of Kansas Prasun Dewan: Towards Generalized Multi-User Editing Purdue University John Ledyard: Coordination Technology California Institute of Technology James Navarro: Computer-Supported Self-Managed Teams Hewlett Packard Tuesday, March 30 Lynda Applegate: "Dyer/Brown & Associates" Case Harvard Business School Industry Panel Discussion: Discussion of "Dyer/Brown & Associates" Case John Henderson: Measuring the Value of Information Technology Investment: Applying an Option Pricing Framework Boston University Hemant Bhargava: Integrating Operations Research Modeling with Collaborative Systems Naval Postgradute School Safaa Hashim: Design of a Customer Service System as a Collaborative System Bull Corporation Additional information can be abtained by calling R.G.K. Foundation. phone #: 512-474-9298 Fax #: 512-499-0245. ------------------------------------------------------------------------ Subject: Technical Report Announcement Date: Thu, 19 Nov 92 19:22:04 +0100 From: Innes Ferguson Fellow DAIers, My October '92 PhD dissertation is now available as Innes A. Ferguson. TouringMachines: An Architecture for Dynamic, Rational, Mobile Agents. Technical Report 273, Computer Laboratory, University of Cambridge, UK, November, 1992, vii + 206 pp. This can be ordered from Lewis Tiffany via tech-reports@uk.ac.cam.cl. Regards... Innes The abstract of the dissertation follows: TouringMachines: An Architecture for Dynamic, Rational, Mobile Agents Innes A. Ferguson Abstract The computer-controlled operating environments at such facilities as automated factories, nuclear power plants, telecommunications centres, and space stations are continually becoming more complex. As this complexity grows, it will be increasingly difficult to control such environments with centralised management and scheduling policies that are both robust in the face of unexpected events and flexible at dealing with operational and environmental changes that might occur over time. One solution to this problem which has growing appeal is to distribute control of such operations to a number of intelligent, task-achieving computational agents. Real-world domains are likely to be populated by multiple agents. In such domains agents will typically perform a number of complex tasks requiring some degree of attention to be paid to environmental change, temporal constraints, computational resource bounds, and the impact the agents' shorter term actions might have on their longer term goals. Operating in the real world means having to deal with unexpected events at several levels of granularity --- both in time and space. While agents must remain reactive in order to survive, some amount of strategic and predictive decision making will be required if agents are to coordinate their actions with other agents and handle complex tasks in an effective manner. This dissertation presents a new integrated agent architecture, designed to provide rational, autonomous, mobile agents with the diverse range of behaviours normally required to carry out complex, resource-constrained tasks in dynamic, real-time, multiagent domains. Upon surveying a collection of existing architectures and after due consideration of the requirements for producing effective, robust, and flexible behaviours in a particular class of such domains, the resulting software control architecture --- the TouringMachine agent architecture --- has been designed through integrating a number of deliberative and nondeliberative control functions. Arranged in a layered fashion, the combination of these functions endows agents with a rich collection of reactive, goal-oriented, reflective, and predictive capabilities. In recognition of the complex relationship which exists between an agent's internal configuration, its task environment, and its ensuing behavioural repertoire, the agent architecture has been implemented in conjunction with a feature-rich, instrumented simulation testbed. The testbed, which permits the creation of a diverse set of single- and multiple-agent navigation task scenarios, has been used to evaluate the utility of the architecture and to identify some of its main strengths and weaknesses.