ICT619 Intelligent Systems Topic 8: Intelligent Agents

ICT6192 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

ICT6193 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

ICT6194 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  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  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

ICT6195 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

ICT6196 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

ICT6197 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

ICT6198 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

ICT6199 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 -> Hi, I am Cybelle. What is your name?

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ICT61911 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

ICT61912 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

ICT61913 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

ICT61914 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

ICT61915 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

ICT61916 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, management.

ICT61917 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)

ICT61918 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

ICT61919 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

ICT61920 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

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ICT61922 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 1.User-programmed agents - write specialised scripts 2.Knowledge-based agents 3.Machine-learning approach

ICT61923 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

ICT61924 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.

ICT61925 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

ICT61926 Building agents – the machine learning approach  Metaphor of a personal office assistant  Agents start with minimum knowledge and learn from: 1.Observation and imitation of user 2.User feedback – direct, indirect 3.Training by user 4.Other agents  User can build up model of agent decision making – more trust  Agent capable of explanation

ICT61927 Development of an agent through learning

ICT61928 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 handling  Agent for meeting scheduling  Agent for electronic news filtering  Agent for recommending books, music

ICT61929 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

ICT61930 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

ICT61931 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

ICT61932 Examples of on-line shopping framework with agent mediation PERSONA Logic Firefly Bargain Finder AuctionBotJango Auction Bot Need identification Product brokering **** Merchant brokering *** Negotiation*** Payment & delivery Service & Evaluation

ICT61933 Examples of on-line shopping framework with agent mediation

ICT61934 Examples of on-line shopping framework with agent mediation

ICT61935 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 plications/Electronic_Commerce/index.shtml plications/Electronic_Commerce/index.shtml plications/Electronic_Commerce/index.shtml for more

ICT61936 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  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

ICT61937 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

ICT61938 REFERENCES  Chin, D., Intelligent Interfaces as Agents. In Intelligent User Interfaces, J. Sullivan and S. Tyler(eds), ACM Press, New York,  Hendler, J., Making Sense out of Agents, IEEE Intelligent Systems, March/April 1999, pp  Hendler, J., Is There an intelligent Agent in Your Future? http//  Maes, P., Agents that Reduce Work and Information Overload, Communications of the ACM, Volume 37, Issue 7 (July 1994), pp pp.  Maes, P., Agents that Buy and Sell, Communications of the ACM, Volume 42, Issue 3 (March 1999), pp pp.  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 