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					Intelligent agents and robots
I. What are agents and bots?
  • Characteristics of agents and bots
  II. How do they work?
     • The structure of agents and bots
     III. Examples of agents and bots
         • Resource discovery tools
         • Agent mediated ecommerce
         • Turing tools
         IV. The future of agents

                                    Introduction to Informatics - Fall 02
Searching for information
I. What are agents and bots?
An agent is a software tool for digging through data
 The software becomes an extension of the user,
 performing tasks on the user's behalf
There are many agents and bots on the web
 Search engines use robots to crawl the web and
 compile the lists of URLs that are the heart of every
 search engine
 Shopping bots compile enormous databases of
 products sold at online stores
Botspot. (2002). What is a bot?
 http://www.botspot.com/
                                     Introduction to Informatics - Fall 02
An agent is software that
 Is autonomous and can be personalized
 Is intelligent and can learn
 Can perceive user actions and act though the user
 interface to engage in discourse
 Presents a human-like appearance and engenders risk
 and trust
 Is context- and domain-sensitive
 Has specialized knowledge
 Is an assistant and not a toolbox


                                     Introduction to Informatics - Fall 02
Agents and bots can be used for
 Information search and retrieval
 Notifier agents, personalized newspapers
 Organizers
 Ecommerce agents
 Context-sensitive help
 Collaborative agents
 Filtering agents
 Reconnaissance agents
 Turing-test agents


                              Introduction to Informatics - Fall 02
Autonomy
An agent should have a measure of autonomy from its
user
 It can pursue an agenda independently of its user
 This requires periodic action, spontaneous execution,
 and initiative
The agent must be able to take preemptive or
independent actions that will eventually benefit the user
 This occurs without the user’s direct intervention



                                       Introduction to Informatics - Fall 02
Personalization
Agents should help people to do some task better
Because of variability in the way we accomplish tasks,
agents must be able to adapt to our different styles
 This requires learning
 The agent should not have to be programmed to handle
 the same task in different ways (observation)
 Once “learned,” this information should be retained
 (memory”)
This allows “persistence of interest”
 You shouldn’t have to restate your interests each time

                                        Introduction to Informatics - Fall 02
Discourse
There should be some communicative interaction with the
agent
 This ensures that the agent is operating according to our
 agenda
 It will accomplish the task as we want it done
The interaction results in a “contract”
 This two way exchange of information establishes
 intentions and abilities
 It may be a single conversation
 It may be a high level discourse with the user and agent
 repeatedly interact with both parties remembering
 previous interactions
                                          Introduction to Informatics - Fall 02
Risk and trust
We delegate tasks to our agent
We have to be trust that the agent will carry out the task
according to our specifications
 This does not occur without risk
 The agent may do what we want but because of its
 autonomy, it may not
We balance the risk that the agent will do something
wrong with the trust that it will do it right
 Our internal mental model of what the agent will do
 determines how much we trust it
 Our domain of interest determines how much a mistake
 will cost us
                                       Introduction to Informatics - Fall 02
Domain
Where we use the agent is the domain of interest
 This is crucial to calculate risk and trust
 In a game or a social pursuit, agent failures carry low risk
 and we tend to have greater trust
 For stock trading or auctions, agent failures are costly
Graceful degradation
When communication does not work, agents should try to
do as much as they can before ending the exchange
 Communications mismatch: the two parties do not
 necessarily communicate well, and may not realize it
 Domain mismatch:one or both parties are out of their
 element, and may not realize it
                                         Introduction to Informatics - Fall 02
Intelligent agents and robots
I. What are agents and bots?
  • Characteristics of agents and bots
  II. How do they work?
     • The structure of agents and bots
     III. Examples of agents and bots
         • Resource discovery tools
         • Agent mediated ecommerce
         • Turing tools
         IV. The future of agents

                                    Introduction to Informatics - Fall 02
Types of agents and bots
Integration
 Information integration, knowledge sharing
Coordination
 Cooperative problem-solving, multi-agent systems
Mobility
 Mobile agent/object solutions
Assistants
 Personal assistants, softbots, data mining
Believable agents
 A-life, simulation

                                   Introduction to Informatics - Fall 02
There are static and mobile agents
 A static agent does all of its work in one place
  An email client that downloads your mail to your PC
 A mobile agent can operate on a network
  It is “sent out” on a mission, finds information and
  reports back
  It traverses the network, executing tasks at each node,
  interacting with other agents that it meets
 Mobile agents are useful in data mining because they
 can find patterns in large data sets
  Data mining is iterative and labor intensive - agents
  save time, refining the search over time and making
  decisions based on past experiences
                                        Introduction to Informatics - Fall 02
Mobile agents are based on a model combining behavior,
state, and location
 Agents inherit a subset of behaviors from the model
   These define the means by which they move through
   the network
 Models also define a method of interagent messaging
The model defines a set of events of interest to the agent
 Arriving at a new location is an important event in the
 life of an agent
 This will entail the invocation of one or more of its
 behaviors
Sommers, B. (1999). Agents: Not just for Bond anymore
http://www.javaworld.com/javaworld/jw-04-1997/jw-04-agents.html

                                            Introduction to Informatics - Fall 02
This is a typical set of events used in a model:
Creation
 This is a “constructor” event
 It brings the agent “to life”
 A handler for this event initializes the agent’s state and
 prepare it for further instructions
Disposal
 This is a “destructor” event
 A handler for this event frees the resources the agent
 is using and prepare the agent for burial
 This is invoked when the agent has completed its task

                                       Introduction to Informatics - Fall 02
Dispatch
 Signals the agent to prepare for departure to a new
 location
 This is generated by the agent itself with a request to
 migrate
 It can be triggered by another agent asking this agent
 to move
Arrival
 Signals that the agent has arrived at its new location
 and is beginning to perform its task
Communication
 Notifies the agent to handle messages incoming from
 other agents ( interagent correspondence)
                                       Introduction to Informatics - Fall 02
             An example: Mail carrier agent lifecycle:

1. Post Office spawns
an army of mail
carriers
2. Each migrates to
first destination in
itinerary
3. It interacts with
agents at first stop,
delivers the mail, and
continues to the next
stop
4. It returns to Post
Office


  Sommers, B. (1999). Agents: Not just for Bond anymore
  http://www.javaworld.com/javaworld/jw-04-1997/jw-04-agents.html
                                             Introduction to Informatics - Fall 02
Classes of agent applications
User passivity/data timeliness
 Applications demand an immediate reaction to incoming
 real-time data streams
 The agent is a digital proxy for the user, interacting with
 data on the user's behalf
Multi-staged/multi-processed calculations
 Calculations are broken into discrete units
 Each unit is assigned to an agent, which is dispatched to
 an “agent farm” where the work is performed
 Upon completion, each agent returns and the results are
 aggregated and summarized

                                        Introduction to Informatics - Fall 02
Untrusted collaborators
 Mobile agents collaborate by meeting in “neutral turf”
 They do this with well-defined interfaces and are
 protected from intrusion or inspection by other agents by
 the agent host
Low-reliability/partially-disconnected networks
 Agents move executable content to a data source rather
 than repeatedly attempting network connections to the
 data source
 An example is a network where users rely on laptops
 networked via dial-up connections
  Prior to disconnecting, agents are sent to a server to
  perform offline calculations
  Upon reconnecting, the agents return to the laptop
                                       Introduction to Informatics - Fall 02
How do agents learn?
If the agent can take instructions
 We teach them by telling them what we want them to do
 They watch us to learn what it is that we do
Other ways agents can learn
 Neural networks
 Statistical methods
 Data mining



                                     Introduction to Informatics - Fall 02
Intelligent agents and robots
I. What are agents and bots?
  • Characteristics of agents and bots
  II. How do they work?
     • The structure of agents and bots
     III. Examples of agents and bots
         • Resource discovery tools
         • Agent mediated ecommerce
         • Turing tools
         IV. The future of agents

                                    Introduction to Informatics - Fall 02
Types of bots
 Chat Bots          News Bots
 Commerce Bots      Newsgroup Bots
 Data Mining Bots   Search Bots
 E-Mail Bots        Shopping Bots
 Fun Bots           Software Bots
 Game Bots          Stock Bots
 Government Bots    Surveillance Bots
 Knowledge Bots     Update Bots


                          Introduction to Informatics - Fall 02
Resource discovery agents
ACORN (Agent-Based Community Oriented Retrieval
Network)
 http://ai.iit.nrc.ca/ll.public/acorn.html
An architecture for the sharing, search, and provision of
information across networks
 Several agents work together to ensure that their users
 get the information that they are interested in
 They can act when user sets a task
  This is a traditional search task
 They can also act with prior instructions
  Agents perform continual community-based browsing

                                             Introduction to Informatics - Fall 02
BFS Spider
 http://ai.bpa.arizona.edu/~mramsey/SPIDER
The Best-First Search (BFS) bot/spider crawls the Web in
search of pages which interest you
You tell it what type of homepages you are interested in
by giving it URLs to start from
 It will crawls the web by following these links and links
 on these pages
 It reports back to you on homepages of interest
 At each page the spider pauses and compares it with
 the original pages
  Those pages which are most similar to the original
  pages get a higher ranking

                                             Introduction to Informatics - Fall 02
Letezia is an interface and reconnaissance agent for web
browsing
 It acts as a scout
 It watches what you look at , records URLs, and infers
 your preferences
When it understands the types of pages that you search
for, it looks through the “neighborhood” for similar pages
 The longer it has to examine your browsing behavior,
 the better it can match your preferences
 Your positive and negative feedback teaches through
 explanation
The interface is a web page with links

                                         Introduction to Informatics - Fall 02
Search engines are examples of agent technology
They act in response to user requests and have
considerable autonomy
You send them into action but cannot use the interface
while they are working
You must state your request explicitly
While you are using the browsing interface, the search
process is idle




                                         Introduction to Informatics - Fall 02
Agent mediated commerce
NativeMinds
 http://an1-sj.nativeminds.com/default.html
NativeMinds features virtual representatives (vReps)
created using the NeuroServer Suite
 These bots can answer any questions you have about
 NativeMinds and its virtual representative technology
 They use facial expressions that match their responses
 vReps such as Nicole provide a best-fit response in
 conversational language
 Nicole will even remember your name each time you visit
 the site


                                              Introduction to Informatics - Fall 02
mySimon
 http://www.mysimon.com/index.jhtml
This agent does comparison shopping across the web
mySimon uses VLA (Virtual Learning Agent) technology
 The agent imitates human navigational behavior
 mySimon's staff of shoppers surfs the Web and
 interacts with the VLA system
 The system translates human navigation behavior into
 MySimon's proprietary programming language
  In this way, it can be “taught” to shop at ecommerce
  sites in hundreds of product categories
Simon shops in real time, so he “always finds the right
products, at the right place, at the best price”
                                      Introduction to Informatics - Fall 02
An agent called Julia, written by Loren Mauldin is an
example of a Turing agent
http://fuzine.mt.cs.cmu.edu/mlm/julia.html
 It is in a class of agents called “Maas-Neotek”
 Julia is a “client bot” TinyMUD robot and can run on
 TinyMUDs, MOOs, or MUSHes
 She connects as any human player on the mud would,
 via a telnet connection
   She does not run on the mud server itself
 She is written in C and runs on a workstation in
 Pittsburgh
 In 1993 Julia ran on DruidMUCK and elsewhere on the
 Internet

                                             Introduction to Informatics - Fall 02
You interact with Julia as you would with any person in
the MUD

>page julia                                                                You
sent your summons to Julia.                                       Julia pages
from Stevi's Kitchen: `I'm on my way to you, Lenny.’
                                              Julia is briefly visible through
the mist.                    Julia says, `I was called here by Lenny.’
           You say, `julia?’
          Julia says, `Yes?’
           You say, `julia?’
          Julia nods to Lenny.
[...]
Julia whispers, `Excuse me, Xerxes is paging me from Connie's place.’
                                                   Julia walks south to the
airship landing field.   Julia has left.



                                                     Introduction to Informatics - Fall 02
Intelligent agents and robots
I. What are agents and bots?
  • Characteristics of agents and bots
  II. How do they work?
     • The structure of agents and bots
     III. Examples of agents and bots
         • Resource discovery tools
         • Agent mediated ecommerce
         • Turing tools
         IV. The future of agents

                                    Introduction to Informatics - Fall 02
IV. The future of agents
Q: Are there any problems that you see agents, and
agents alone, solving in the next few years?
A: Information overload
 Users are increasingly dealing with vast amounts of
 information that is unstructured and very dynamic
 In order to keep track of everything, and in order to
 find the information relevant to THEM, they will have
 to use software that knows their interests and can act
 on their behalf
Pattie Maes
IC Online Virtual Roundtable: The Future of Software Agents
http://computer.org/internet/v1n4/round1.htm

                                           Introduction to Informatics - Fall 02
Q: What other technologies do you think will have the
greatest impact on the directions of agent technology?


A: Learning and planning technologies
If you consider an agent to be a sophisticated surrogate or
advisor, there’s enormous potential to be had from both
learning and planning
 Learning has obvious applications to agent creation and
 refinement
 Planning technology, which addresses the automated
 creation of sequences of activity to satisfy goals, will
 have an equally significant role to play in future agents
Jeff Rosenschein

                                        Introduction to Informatics - Fall 02
A: Agents are components that execute in a distributed
heterogeneous and unstructured environment
 So, the most important relevant technologies are
 software component and distributed computing
 technologies, both of which are progressing rapidly
Mani Chandy
A: If we consider mobile agents from a performance
perspective, operating systems that would support
process launch/migration/shut-down quickly, and network
protocols that can be used for signalling and efficient
discovery of mobile objects
 From an intelligent agent perspective, any technology
 that deals with common sense understanding and
 learning in a practical way
Sankar Virdhagriswaran
                                      Introduction to Informatics - Fall 02
A: Human-computer interaction design
 Agents will not be accepted unless users feel they can
 trust them
 We have to learn how we can build agents that users
 can understand and control
Pattie Maes
A: The most needed technology is a revolutionizing
agent user interface that makes agents (from different
providers) as easy and fun to use as Web browsers
make the Web fun to surf
 Secondly, we need an agent interaction language that
 allows (1) the endures to communicate with agents,
 and (2) agents to communicate with each other in a
 uniform manner
Danny Lange
                                      Introduction to Informatics - Fall 02

				
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