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Autonomous Intelligent Research Robots Maulik Shah & Sandeep Malalur May 01, 2000 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. 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. 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 clientserver side programs (applets, scripts…) and interacts with user with a natural language processing unit (NPL) 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: http://www.sce.carleton.ca/netmanage/docs/AgentsOverview/ao.html 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 KQMLused for co-ordination with other agents. Softbots Software Robots Perform tasks on user’s behalf Finding information Filtering email Scheduling meetings Effectors mv, ftp… Sensors ls, finger… Reference: http://www.cis.udel.edu/agents98/ 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. 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” 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: http://pattie.www.media.mit.edu/people/pattie/ECOM/index.htm 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) 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). 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. 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. 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. 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. 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? 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. 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. 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. 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. 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 Developments and Challenges (Contd.) … State of the Art (Contd.) … Growing heterogeneity Economies of scale (speed, bandwidth issues) Reference: http://www.sce.carleton.ca/netmanage/docs/AgentsOverview/ao.html 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. 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. 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…. 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. Amazon.com PersonaLogic.com Barnes and Noble (Recommendation Agents) 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. 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. 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 http://www.jango.excite.com 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) 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. References Les Gasser, 1998. “Agents in Rational Structure of Scientific Research”, National Science Foundation. http://www.cis.udel.edu/agents98/LG-agents98-talk/ppframe.htm 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' Agents www2.computerworld.com/home/online9697.nsf/All/970630agents 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 Jansen http://131.193.153.231/issues/issue2_3/jansen/ 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. http://www.msci.memphis.edu/~franklin/AgentProg.html AARIA: Autonomous Agents at Rock Island Arsenal http://www.aaria.uc.edu/overview.html References(Contd.)… Agent-Based Engineering, the Web, and Intelligence by: Charles J. Petrie, Stanford Center for Design Research. http://cdr.stanford.edu/NextLink/AID.html Chronological overview of expected/ predicted developments. http://www.broadcatch.com/agent_thesis/h622.htm The Agent Technique http://www.broadcatch.com/agent_thesis/h62.htm The User http://www.broadcatch.com/agent_thesis/h63.htm References(Contd.)… Software Agents and the Future of Electronic Commerce http://pattie.www.media.mit.edu/people/pattie/ECOM/index.htm Intelligent Systems: Robots, Autonomous Agents, Agent Societies http://www.cs.byu.edu/info/mikeg/research/Research.html Hyacinth S. Nwana, “Software Agents: An Overview” http://www.sce.carleton.ca/netmanage/docs/AgentsOverview/ao.html