ICT619 Intelligent Systems

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ICT619 Intelligent Systems

• Topic 8 Intelligent Agents

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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

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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

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What is an intelligent agent? (contd)

• 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

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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

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What intelligent agents can do for us

• Carry out tasks on the users 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

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What intelligent agents can do for us (contd)

• 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

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What intelligent agents can do for us (contd)

• 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

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What intelligent agents can do for us (contd)

• 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
  -gt

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What intelligent agents can do for us (contd)

• 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

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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

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Characteristics of a good agent (cont.)

• Communication
• Must communicate well with the user
• Should understand users 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

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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

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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

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Types of agents (contd)

• 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.

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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)

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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

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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

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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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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

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Building intelligent agents (contd)

• 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 agents knowledge by updating rule base
  with time
• The issue of trust is then reduced to users
  trust in their own programming skills

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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 users 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.

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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
• Users trust is affected as agent is programmed
  by someone else

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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

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Development of an agent through learning
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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

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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

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Intelligent agents in E-Commerce (contd)

• 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

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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

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Examples of on-line shopping framework with agent
mediation
PERSONA Logic Firefly Bargain Finder Auction Bot Jango Auction Bot T_at_T
Need identification
Product brokering
Merchant brokering
Negotiation
Payment delivery
Service Evaluation
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Examples of on-line shopping framework with agent
mediation
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Examples of on-line shopping framework with agent
mediation
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Examples of on-line shopping framework with agent
mediation (contd)

• 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_Soft
  ware/Applications/Electronic_Commerce/index.shtml
• for more

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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

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Intelligent agent design - state-of-the-art and
future (contd)

• 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

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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/webmatter
  s/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/