Autonomous Intelligent Research Robots

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Autonomous Intelligent Autonomous Intelligent Research RobotsResearch Robots

Maulik Shah & Sandeep MalalurMaulik Shah & Sandeep MalalurMay 01, 2000May 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 client-server 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 KQML-

used 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”

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

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.

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.

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' Agentswww2.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 Jansenhttp://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 Arsenalhttp://www.aaria.uc.edu/overview.html

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 Techniquehttp://www.broadcatch.com/agent_thesis/h62.htm

The Userhttp://www.broadcatch.com/agent_thesis/h63.htm

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

Autonomous Intelligent Research Robots

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