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ICT619 Intelligent Systems

ICT619 Intelligent Systems. Topic 8: Intelligent Agents. Intelligent Agents. What is an intelligent agent? Why intelligent agents? What intelligent agents can do for us Characteristics of a good agent Types of agents Building intelligent agents Intelligent agents in E-Commerce

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ICT619 Intelligent Systems

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  1. ICT619 Intelligent Systems Topic 8: Intelligent Agents

  2. Intelligent Agents • What is an intelligent agent? • Why intelligent agents? • What intelligent agents can do for us • Characteristics of a good agent • Types of agents • Building intelligent agents • Intelligent agents in E-Commerce • Intelligent agent design - state-of-the-art and future ICT619

  3. What is an intelligent agent? Underlying concept - • An autonomous computational entity designed to perform a specific task, without direct initiation and continuous monitoring on part of the user • Emerged in the last 15 years or so • Distinct from conventional programs, in that it is automatic Additional properties: • Some level of intelligence (based on any AI technology from fixed rules to learning engines) for decisions and/or adaptation to environmental change • Acts reactively, but also proactively • Social ability - communicates with user, system, other agents as required • Might cooperate with other agents to carry out complex tasks • Agents might move from one system to another to access remote resources and/or meet other agents ICT619

  4. What is an intelligent agent? (cont’d) • Intelligent agents (also called “software agents”) do not necessarily possess all these possible features • Wide range of variation in capabilities: • Some perform tasks individually while others are cooperative • Some are mobile- able to move across a network, others are not • Most communicate via coded messages or even natural language, some don't communicate at all • Multiple agents work in groups or swarms to solve problems collectively, some work as individual units • Not all agents learn and adapt themselves • Robots are physically embodied agents ICT619

  5. Why intelligent agents? • More and more everyday tasks becoming computer-based • An increasing number of untrained users using computers • Current human-computer interfaces require users to initiate all tasks and monitor them - manually • Intelligent agents engage in a cooperative process with the user to leverage the effectiveness and efficiency of human-computer interaction • Staggering growth in information availability • Intelligent agents can be a tool for relieving the user of this information overload • Intelligent agents can act as personal assistants to the user to manage information • Might one day take over routine tasks in personal management such as appointments, meetings and travel arrangements ICT619

  6. What intelligent agents can do for us • Carry out tasks on the user’s behalf • Train or teach the user • Help different users collaborate • Monitor events and procedures • Specifically, intelligent agents can help us with • Information retrieval • Information filtering • Mail management • Recreational activities – selection of books, music, holidays • Booking of meetings, hotels, tickets ICT619

  7. What intelligent agents can do for us (cont’d) Information filtering agent • One type is the selection of articles from a continuous stream to suit particular user needs • User can create “news agents” and train them by giving positive or negative feedback for articles recommended • The use of key words alone can be restrictive • Underlying semantics must be extracted for more effectiveness • Eg VPOP Technologies' Newshub - an automated, agent-based web news feeder service, which delivers customised updates of stories from major news outlets every 15 minutes ICT619

  8. What intelligent agents can do for us (cont’d) Electronic mail agent • Assist users with electronic mail • Learn to prioritize, delete, forward, sort and archive mail messages on behalf of the user • May use intelligent system techniques like case-based reasoning • Can associate a level of confidence with its action or suggestion • Use of “do-it” and “tell-me” thresholds set by user • May involve multi-agent collaboration ICT619

  9. Hi, I am Cybelle. What is your name? What intelligent agents can do for us (cont’d) Selection agents for entertainment • Conversational agents show potential for becoming popular and commercially successful eg Cybelle, ALICE • Use “social filtering” – correlation between different users to make recommendations on books, CDs, films etc. • So, if user A liked items X and Y, and user B liked item X and Z, then item Z may be recommended for user A • Amazon.com has been using this system for years -> ICT619

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  11. What intelligent agents can do for us (cont’d) Some other current and emerging applications of intelligent agents: • air traffic control • air craft mission analysis • control of telecommunications and network systems • provision and monitoring of medical care • monitoring and control of industrial processes • on-line fault diagnosis and malfunction handling • supervision and control of manufacturing environments • transactions management in banks and insurance companies • E-commerce, tourism ICT619

  12. Characteristics of a good agent Action • Agent must be able to take some action and not just provide advice • Present state of web technology limits capability of Internet agents - still no standard interface for agents, but agent communication languages such as ACL and KQML might win out • As the Internet becomes more agent-friendly, more capable agents will emerge Autonomy • An agent can be much more useful if it can act autonomously • The right level of autonomy for a task must be found ICT619

  13. Characteristics of a good agent (cont.) Communication • Must communicate well with the user • Should understand user’s goals, preferences and constraints • Useful communication requires shared knowledge on • language of communication • problem domain Example Problem: Web search engines • accept key words and phrases (some knowledge of the language) but • understand nothing about the documents they retrieve (no domain knowledge) • Solution: provision of a machine-readable ontology - a definition of a body of knowledge including its components and their relationships ICT619

  14. Characteristics of a good agent (cont.) Adaptation • Can gain user confidence by learning user preferences • ML techniques such as ANNS, GAs or CBR can be used • Adapting to user preferences can be also achieved by using data mining techniques such as clustering • Agent forms clusters of users with similar features • User's needs can then be anticipated by placing the user in one of these clusters and analysing the cluster • Social problem solving method, similar to Amazon recommendations ICT619

  15. Types of agents • Based on operational characteristics and functional objectives: • Collaborative agents • Work together to - integrate information and - negotiate with other agents to resolve conflict - Provide solutions to inherently distributed problems, e.g., air traffic control • Reactive agents • Act by stimulus-response to the current state of the environment • Each reactive agent is simple and interacts with others in a basic way ICT619

  16. Types of agents (cont’d) Interface agents • Provide user support and assistance • Cooperate with user in accomplishing some task in an application. • Interface agents learn: • by observing and imitating the user • through receiving feedback from the user • by receiving explicit instructions • by asking other agents for advice (from peers) • Examples: • Personal assistants performing information filtering, email management. ICT619

  17. Types of agents (cont.) Mobile agents • Programs that migrate from one machine to another. • Execute in a platform-independent execution environment, like Java applets running on a Java virtual machine • Practical but non-functional advantages: • Reduced communication cost • Asynchronous computing (when you are not connected) ICT619

  18. Types of agents (cont.) Two types of mobile agents: • One-hop mobile agents (migrates to one other place) • Multi-hop mobile agents (roam the network from place to place) Example applications: • Distributed information retrieval • Telecommunication network routing ICT619

  19. Types of agents (cont.) Information agents • Manage information • Manipulate or collate information from many distributed sources. • Can be mobile or static. • Examples: • BargainFinder compares prices among Internet stores for CDs • Jasper works on behalf of a user or community of users and stores, retrieves and informs other agents of useful information on the WWW ICT619

  20. Types of agents (cont.) Multiple agent systems • Consist of collections, or swarms, of simple agents that interact with each other and the problem environment • Can be mobile or static, same or different agents • Complex patterns of behaviour emerge from collective interaction • Examples: • Swarm of bees finds an optimal location for the hive • xxxx ICT619

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  22. Building intelligent agents Two main problems to overcome: • Competence • How do we build agents with the knowledge needed to decide • when to help the user • what to help the user with, and • how to help the user? • Trust • How to guarantee user comfort (and protection!) in delegating tasks to the agent • Approaches to building agents • User-programmed agents - write specialised scripts • Knowledge-based agents • Machine-learning approach ICT619

  23. Building intelligent agents (cont’d) • The main problem with user-programmed approach - requires high level of user competency - user must be able to • Recognise opportunity for employing an agent • Take initiative to create an agent • Impart specific knowledge to agent by codifying it in a special language • Maintain agent’s knowledge by updating rule base with time • The issue of trust is then reduced to users’ trust in their own programming skills ICT619

  24. Building intelligent agents (cont.) In the knowledge-based approach, • The agent is supplied with knowledge about the application and user • At run-time, agent uses the knowledge to recognise user’s plans and find opportunities to contribute to them • Example of knowledge-based agent: the UCEgo - designed to help users solve problems in using the UNIX operating system. ICT619

  25. Building intelligent agents (cont.) Problems with knowledge-based approach - • Both competence and trust are issues of concern • The problem of competence relates to the competence of the knowledge engineer • Knowledge-base is fixed and cannot be customised to specific user needs • User’s trust is affected as agent is programmed by someone else ICT619

  26. Building agents – the machine learning approach • Metaphor of a personal office assistant • Agents start with minimum knowledge and learn from: • Observation and imitation of user • User feedback – direct, indirect • Training by user • Other agents • User can build up model of agent decision making – more trust • Agent capable of explanation ICT619

  27. Development of an agent through learning ICT619

  28. Building agents – the machine learning approach Advantages: • Less work from end-user and developer • Agent customises to user/organisation habits/preferences • Helps distribute know-how and competence among different users Some examples: • Agent for e-mail handling • Agent for meeting scheduling • Agent for electronic news filtering • Agent for recommending books, music ICT619

  29. Intelligent agents in E-commerce • Rapid growth continues in e-commerce • Information about products and vendors is easily accessible • But transactions are still mostly not automated • Six fundamental stages of the buying process: • Need identification • Product brokering • Merchant brokering • Negotiation • Purchase and delivery • Product service and evaluation ICT619

  30. Intelligent agents in E-Commerce (cont’d) • In the need-identification stage, agents can help in purchases that are repetitive or predictable • Continuously running agents can monitor a set of sensors or data streams and take actions when certain pre-specified conditions apply • Agents can use rule-based systems or data mining techniques to discover patterns in customer behaviour to help customers find products ICT619

  31. Intelligent agents in E-commerce (cont.) • In the merchant brokering stage, on-line shopping agents can look up prices for a chosen product for a number of merchants • Many business-to-business transactions are canvassed • In a web auction, customers are required to manage their own negotiation strategies • Intelligent agents can help with this ICT619

  32. Examples of on-line shopping framework with agent mediation ICT619

  33. Examples of on-line shopping framework with agent mediation ICT619

  34. Examples of on-line shopping framework with agent mediation ICT619

  35. Examples of on-line shopping framework with agent mediation (cont’d) • Software agents are helping buyers and sellers cope with information overload and expedite the online buying process • Agents are creating new markets (eg, low-cost consumer goods) and reducing transaction costs • Use of agents in e-commerce still at an early stage • Visit http://agents.umbc.edu/Applications_and_Software/Applications/Electronic_Commerce/index.shtml for more ICT619

  36. Intelligent agent design - state-of-the-art and future • Few agents are available with all the desired characteristics • Agent technology still in experimental stage • Autonomy and mobility already achievable • Example: Java applets which execute independently across networks • But autonomy limited so far in practical use due to the agent-unfriendliness of the current web technology ICT619

  37. Intelligent agent design - state-of-the-art and future (cont’d) • A major limiting factor is lack of ontologies essential for effective communication • Building and maintaining ontologies remains a major challenge • Some of the proposed capabilities to be developed in future intelligent agents include: • Learning as well as reasoning, which are characteristics of machine intelligence • Interacting with the external environment through sensors ICT619

  38. REFERENCES • Chin, D.,Intelligent Interfaces as Agents. In Intelligent User Interfaces, J. Sullivan and S. Tyler(eds), ACM Press, New York, 1991. • Hendler, J., Making Sense out of Agents, IEEE Intelligent Systems, March/April 1999, pp.32-37. • Hendler, J., Is There an intelligent Agent in Your Future? http//www.nature.com/nature/webmatters/agents/agents.html • Maes, P., Agents that Reduce Work and Information Overload, Communications of the ACM, Volume 37 , Issue 7 (July 1994), pp. 30-40. • Maes, P., Agents that Buy and Sell, Communications of the ACM, Volume 42 , Issue 3 (March 1999), pp. 81-91. • Sheth, B. and Maes, P. Evolving Agents for Personalized Information Filtering. In Proceedings of the Ninth Conf. on Artificial Intelligence for Applications. IEEE Computer Society Press, 1993 • UMBC Agent News - http://agents.umbc.edu/agentnews/current/ • http://www.agentland.com/ ICT619

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