ICT619 Intelligent Systems
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Transcript 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
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?
(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
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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 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
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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
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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
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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
Hi, I am Cybelle.
What is your name?
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 ->
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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
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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 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
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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 basicICT619
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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.
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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
1. User-programmed agents - write specialised scripts
2. Knowledge-based agents
3. Machine-learning approach
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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
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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 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.
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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
User’s 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:
1.
2.
3.
4.
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
(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
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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 Auction
Finder
Bot
*
*
Jango Auction
Bot
T@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 (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/Ap
plications/Electronic_Commerce/index.shtml
for more
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Intelligent agent design - state-ofthe-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-ofthe-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
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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/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/
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