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Autonomous Intelligent Research Robots

Autonomous Intelligent Research Robots

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Autonomous Intelligent Research Robots

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  1. Autonomous Intelligent Research Robots Maulik Shah & Sandeep Malalur May 01, 2000

  2. What are Autonomous Intelligent Research Robots? • Autonomous? • Lie within the environment of the system • Employ Artificial Intelligence (AI) techniques • Act on complex and dynamic environment between the user and the system resources.

  3. Autonomous Intelligent Research Robots? (Contd.)… • Intelligent? • Assists user with the applications assigned as a set of goals and tasks. • Can make decisions based upon simulation of needed solutions. • Can determine choices based upon experience.

  4. How they work? • Internet lacks semantic information • HTML specifies …how to display info without specifying its meaning • Internet is a dynamic structure… page contents are not static • …hence these robots’ perception are that web pages are written in HTML, I/P-O/P of client-server side programs (applets, scripts…) and interacts with user with a natural language processing unit (NPL)

  5. How they work?(Contd.)… • Lie within the environment of the system • New Agent – autonomous clustering • Changes goal-oriented behavior based on neutral clustering to find other agents on the LAN. Reference:

  6. How do they communicate? • Using languages • Blackboard: Read and write messages in shared location. • Knowledge Query and Manipulation Language (KQML): Protocol for information and knowledge exchange. • Knowledge Interchange Format (KIF) • COOL: Structured conversation based on KQML- used for co-ordination with other agents.

  7. Softbots • Software Robots • Perform tasks on user’s behalf • Finding information • Filtering email • Scheduling meetings • Effectors • mv, ftp… • Sensors • ls, finger…Reference:

  8. Potential of Robots • Crawl from one server to another, compiling lists of URLs to find information and report back. • Traverse Web’s hypertext structure by retrieving a document and recursively retrieving others which are referenced. • Maintain a hypertext structure that can be checked for “dead links”. • Example: CERN HTTPD servers log failed requests caused by dead links, with the preference to the page where dead link occurred.

  9. Potential (Contd.)… • Performs statistical analysis of retrieved documents and provides resource discovery database. • Operate in parallel, resulting in high use of available bandwidth. • Great potential in “Data Mining”

  10. Potential (Contd.)… • Data Mining • Process of finding patterns in enormous amounts of data. • Requires series of searches. • Makes decisions based on experience to perfect complex searches. Reference:

  11. Potential (Contd.)… • Artificial Intelligence • Develops software capable of processing information on its own without need of human intervention. • Interoperability • Major initiatives that will make agents ubiquitous: • OPS (open profiling standard) • XML (extended markup language) • JEPI (joint electronic payment initiative)

  12. Applications of Autonomous Intelligent Robots • Use in E-Commerce • Support for Wireless Application Protocol (WAP) – enabled net devices and wireless handheld devices using Network Query Language (NQL). • Systematically search commercial sites on the web and capture detailed data (Online Media Network Intelligent Agent – OMNIAC).

  13. Applications (Contd.)… • Personal Research Assistants • User assigns set of rules and preferences to the agent. • Agent acts as an assistant by communicating and understanding user’s preference and achieves assigned tasks. • How? • Scans the database and information resources. • Delivers summaries and information on certain topics base on requests. • Examples: Open Sesame and Microsoft’s Bob.

  14. Applications (Contd.)… • Information Management Assistants (Resource Discovery) • Behaves similar to the Personal Research Assistant. • But they work in complex and dynamic environment between the user and system resources. • Pre-determines data resources. • Example: Oracle’s ConText.

  15. Applications (Contd.)… • As Robot Systems in Engineering Applications • Lie in the engineering and technology field. • These include space and marine applications. • Artificial intelligence (especially agent architectures, machine learning, planning, distributed problem-solving), information retrieval, database and knowledge-base systems, and distributed computing.

  16. Applications (Contd.)… • Internet-based information systems, adaptive (customizable) software systems, autonomous mobile and immobile robots, smart systems (smart homes, smart automobiles, etc.), decision support systems, and intelligent design and manufacturing systems.

  17. Disadvantages ? • They are domain and task dependent. • Solution? • Different knowledge base for different domains. • Uniform way of using the knowledge domain. • Different specialized agents for each domain pair. • Need for uniform framework. • Is it possible?

  18. Problems Encountered • Compatibility with the WWW? • NO! • WWW uses client-server orientation, while agents require peer-to-peer communication. • WWW is oriented around data transport through networks. • They require structure reflecting task level semantics.

  19. Problems (Contd.)… • Bootstrapping • New agent performs autonomous clustering. • Changes its behavior based on results of mutual clustering. • Unable to locate existing agents and initiate conversation. • Slows down server performance. • Communication with other agents existing in the network is a MAJOR problem. • Remedy…ever developing architecture and data paths could resolve the problem in the near future.

  20. Problems (Contd.)… • Client side robot • Cannot fix bugs. • Cannot provide new efficient advantageous facilities. • Cannot add knowledge of problem areas. • Technical issues of vigilance, thrift, secrecy and user privacy. • Lower level tasks - implemented by sensor based controllers (embedded within the overall system architecture). • Integrates real-time operation & aspects of AI.

  21. Current Developments and Challenges • Making decisions based on information availability. • Stability and Performance Issues. • Interoperability and Communication. • Collaborative Research Systems. • Setting up systems which are user customized. • Trust and Competence Issues. • Multi-agent systems which use heterogeneous architecture.

  22. Developments and Challenges (Contd.)… • State of the Art (Information Infrastructure Context) • Connectivity (e.g. Internet/ WWW) • Growing digital content (size, complexity, modality) • Computation intensive (High-performance computers) • Limited Access Modality (Conventional Channels, plugs) • Distributed Resources

  23. Developments and Challenges (Contd.) … • State of the Art (Contd.) … • Growing heterogeneity • Economies of scale (speed, bandwidth issues) Reference:

  24. Examples of Autonomous Intelligent Research Robots • Information Visualizer • Experimental system where the user and the agent system perform communication, monitor events and look after tasks for information retrieval. • It attempts to utilize graphics technology to lower the cost of finding and accessing information. • Harvest • Resource discovery robotic system which allows the users a much controlled way of indexing the Web.

  25. Examples (Contd.)… • ALIVE • Entertainment intelligent agent system which allows users to enter a virtual world and interact with full-body images and animated agents. • Calendar Apprentice (CAP) • Personal assistant agent system which learns about user preferences and habits and manages his calendar operations. The system learns about the user's scheduling and preferences from experience and serves as a personal software assistant.

  26. Examples (Contd.)… • BullsEye • IntelliSeek's BullsEye is a search and retrieval agent that allows you to search multiple search engines (450+) through a single interface. • Integrates many of the tasks power searchers use under one intuitive interface and combines targeted meta-searching with full text analysis (via the Verity 97 toolkit), and filtering, for more relevant results. Works behind a firewall. Windows only. • … and a lot more….

  27. KnowMan • Intelligent software for creating Internet agents. The software comes in complete product packages and as easily embedded components. • Mind It • Formerly "URL-Minder", keeps track of a specified URL and e-mails you (or your readers) when it changes. Can also embed an e-mail form in your web sites so users can be notified when your pages change. • MOMspider • A web-roaming robot written in Perl 4 that automatically checks web sites for bad URLs and indexes sites.



  30. Barnes and Noble (Recommendation Agents)

  31. A Case Study: ShopBot • Internet agent developed by researchers at Washington University. • Still a prototype. • Goal-oriented and assist human user in shopping(virtually at different sites) and presents information extracted from those sites. • Exhibits learning by example and off-line learning.

  32. ShopBot (Contd.)… • Two phases of operation- • Learning phase • Learns how to shop at different sites specified by its creators. (Disadv. Limits flexibility) • Sites must support search forms for ShopBot to learn. • Retrieves info on learning-by-example technique. • Real-time online comparison-shopping phase • Extracts info based on human user query. • Uses experience and learned extraction techniques to compare prices at different vendor sites.

  33. ShopBot (Contd.)… • Advantages • Product-independent • Learns description of a particular domain. • No NLP required. • No Natural language interface required. • Disadvantages • Domain independent in one domain. • Just learns to shop…not efficient shopping ability. • Shop at sites that have search forms. • End user cant specify example sets. • ShopBot is used at

  34. Conclusion • Global resource discovery • Provide data retrieval over LANs and WANs. • Largely incompatible with the web, but future developments in network architecture and data path will resolve the problem. • Multi-user domains • Information retrieval and storage (Metadata) • Internet search (algorithms, indexes, UIs, spiders) • Document/file management • Storage/repositories (text, hypermedia)

  35. Conclusion(Contd.)… • Used for collaboration, electronic commerce, finding, gathering, filtering, management, planning, resource allocation, network management, diagnostics, as personal assistants, process workflow etc. • Goal identification and planning – initial phase. • NLP techniques are not effectively used in the Internet agent framework.

  36. References • Les Gasser, 1998. “Agents in Rational Structure of Scientific Research”, National Science Foundation. • Edited by Gray & Caldwell, 1996. "Advanced Robotics & Intelligent Machines," IEE Control Engineering Series, pp. xvi-xx. • Pattie Maes, 1995. "Artificial life meets entertainment: Lifelike autonomous agents," Communications of the ACM, vol. 38, no. 11, pp. 108-114. • Witold Jacak, 1998. "Intelligent Robotic Systems," IFSR International Series on Systems Science and Engineering, Vol 14, pp 1-4, 10. • The Agents'

  37. References(Contd.)… • Anonymous, 1994. "The age of the Intelligent Agent," Insurance Systems Bulletin, Vol. 9, No. 10, pp. 4-5. • Using an Intelligent Agent to Enhance Search Engine Performance: by James Jansenhttp:// • Is it an Agent, or just a Program?: A Taxanomy for Autonomous Agents by: Stan Franklin and Art Graesser, Institute for Intelligent Systems, University of Memphis. • AARIA: Autonomous Agents at Rock Island Arsenal

  38. References(Contd.)… • Agent-Based Engineering, the Web, and Intelligence by: Charles J. Petrie, Stanford Center for Design Research. • Chronological overview of expected/ predicted developments. • The Agent Technique • The User

  39. References(Contd.)… • Software Agents and the Future of Electronic Commerce • Intelligent Systems: Robots, Autonomous Agents, Agent Societies • Hyacinth S. Nwana, “Software Agents: An Overview”