Intelligent Agents

1
Intelligent Agents

• With Java

2
Focus of talk

• A basic look at agent-based reasoning, modeling,
  and learning
• How agents can enhance the capability and
  productivity of commercial application software
• The effect of agents on the Web, with a Java twist

3
Artificial Intelligence Introduction

• The science of AI is approximately forty years
• dating back to a conference at Dartmouth in 1958
• The public perception of AI has not always
  matched the reality
• The excitement of both scientists and the popular
  press tended to overstate the real-world progress
  of artificial intelligent systems
• Early success promised rapid progress towards
  practical machines intelligence. Areas of early
  successes
• Game playing, mathematical theorem proving,
  common-sense reasoning, college mathematics

4
Introduction, contd.

• AI research labs began specializing in narrow
  fields
• Speech recognition
• Natural language understanding
• Image optical character recognition
• The early successes were followed by a slow
  realization that things that humans do with very
  little effort was near impossible for the
  computer
• What was hard for people and easy for the
  computer was more than offset by the things that
  were easy for people to do but almost impossible
  for computers to do

5
Introduction, contd.

• The promise of the early years has never been
  fully realized
• The term artificial intelligence have become
  associated with failure and over-hyped technology
• Nevertheless, researchers in AI have made
  significant contributions to computer science
• WIMP (Windows, icon, mouse, pointer) user
  interface
• Considered highly controversial and impractical
  when first introduce by the IA community
• Object-oriented programming techniques
• Refinement of the AI Frames concept

6
Basic Concepts

• AI has always focused on problems which lie just
  beyond the reach of state-of-the-art computers
• Effectively pushing the current bleeding-edge
  technologies
• As computer science and computer systems evolved,
  the focus and areas which falls into AI research
  have also changed
• We can identify three major phases of development
  in AI research

7
First Phase

• Much of this work dealt with formal problems that
  were structured and had well-defined problem
  boundaries
• Math related skills proving theorems, geometry,
  calculus, games (checkers, chess)
• Emphasis was on creating general thinking
  machines capable of solving broad classes of
  problems
• These systems tended to include sophisticated
  capabilities relating to reasoning and search
  techniques

8
Second Phase

• Marked by the recognition that the most
  successful AI projects were aimed at very narrow
  problem domains
• These systems usually encoded much specific
  knowledge about the problem to be solved
• This approach of adding specific domain knowledge
  to a more general reasoning system led to the
  commercial success in AI Expert Systems.
• Rule-based expert systems were developed to do
  many tasks
• Chemical analysis, configuring computer systems,
  diagnosing medical conditions in patients
• Suitable for repetitive and hazardous work
• Automated Process Control (Manufacturing Systems)

9
Second Phase, contd.

• Expert systems utilized research in a number of
  AI based discipline
• Knowledge representation, knowledge engineering,
  advanced reasoning techniques
• These systems proved that artificial intelligence
  could provide real value in commercial
  applications
• Expert systems workstations with powerful
  integrated development environments were
  developed
• Lisp, Prolog, Smalltalk
• These were years ahead of commercial software
  development

10
Third Phase

• Since the late 1980s much of the AI community has
  been working on solving some difficult problems
• Machine vision and speech
• Natural language understanding and translation
• Commonsense reasoning and robot control
• Connectionism regained popularity and expanded
  the range of commercial applications through the
  use of neural networks for use in
• Data mining
• Modeling
• Adaptive control

11
Third Phase, contd.

• The AI playing field has been reenergized by
  biological methods such as genetic algorithms and
  alternative logic systems such as fuzzy logic
• Recent explosive growth in the Internet and
  distributed computing has led to the idea of
  Software Agents
• Software Agents are autonomous entities that move
  through the network, interacting with each other
  and performing tasks for their users

12
Intelligent Agents

• Intelligent agents are software agents that use
  the latest AI techniques to provide autonomous,
  intelligent, and mobile software components,
  thereby extending the reach of users across
  networks

13
Foot Note

• Using commercial success as a measure of the
  value of technology is problematic to say the
  least
• I hypothesize that technology that is most
  beneficial to humanity on a whole will be the
  least commercially viable
• The rules of supply and demand will not apply to
  technologies that have the following
  characteristics
• Source is abundant (water for instance)
• The ability to transform and make readily
  available is attainable by every society
• Low technological barrier

14
What do we mean by intelligence?

• Do we mean that our agents acts like a human?
  Think like a human? That it acts or thinks
  rationally?
• There are as many answers as there are
  researchers involved in AI work
• From a software development perspective an
  intelligent agent is one that acts rationally
  primarily from a behavioral view point
• It does the things we do, but not necessarily the
  same way we would do them
• Our agent may not pass the Turing test as a
  yardstick for judging computer intelligence

15
Why AI Failed

• This is only my opinion
• AI as we know it lacks a true model of cognition
  that can shed insights into events such as
• Correlation of facts, inference, and memory
• How the human brain work higher level cognitive
  functions such as reasoning
• The Von Neumann model of a computer is a not a
  reasonable model of the brain and of human
  cognition

16
What do we mean by intelligence?

• Our agents will perform useful tasks for us
• They will make us more productive
• They will allow us to do more work in less time,
  and see more interesting information and less
  useless data
• Our programs will be qualitatively better using
  AI techniques than they would be otherwise
• The humble goal of intelligent agents is to
  develop better smatter applications

17
Areas to Explore

• Symbol processing
• Neural networks
• The Internet and the World Wide Web
• Events-Conditions-Actions

18
Intelligent Agents

• Part-II

19
Intelligent Behavior

• There are many behaviors to which we ascribe
  intelligence
• The ability to recognize situations or cases is a
  type of intelligence
• For example, a doctor who talks with a patient
  and collects information regarding the patients
  symptoms
• Then able to accurately diagnose an ailment and
  the proper course of treatment
• The ability to learn from a few examples and then
  generalize and apply that knowledge to new
  situations is another form of intelligence
• Intelligent behavior can be produced by the
  manipulation of symbols

20
Symbol Processing

• Symbol Processing is an AI technique
• Assertion Intelligent behavior can be produced
  by the manipulation of symbols
• A primary tenets of AI techniques
• Symbols are tokens which represents real-world
  objects or ideas
• In this approach, a problem must be represented
  by a collection of symbols
• An appropriate algorithm must then be developed
  to process these symbols

21
Symbol Processing, contd.

• Physical symbol systems hypothesis
• Newell and Simon 1980
• States that only a physical symbol system has
  the necessary and sufficient means for general
  intelligent action.
• Basic thesis is that intelligence flows from the
  active manipulation of symbols
• This was the cornerstone on which much of the
  subsequent AI research was built
• Research built intelligent systems using symbols
• pattern recognition, reasoning, learning,
  planning
• History has shown that symbols may be appropriate
  for reasoning and planning
• Pattern recognition and learning are suited for
  other approaches

22
Manipulation of Symbols

• Symbols in the formulations of If-Then rules
• Processed using forward and backward chaining
  reasoning techniques
• Forward chaining system deduce new information
  from a given set of input data
• Backward chaining system reach conclusion based
  on a specific goal state
• Semantic Network
• Symbols and the concept they represent are
  connected by links into a network of knowledge
  that can then be used to determine new
  relationships
• Frames similar to Objects in the OO paradigm
• Attributes of a concept are grouped together with
  related procedures for processing

23
Symbol Processing and Cognition

• Symbol processing
• These techniques represent a relatively high
  level in the cognitive process
• Correspond to conscious thought, where knowledge
  is explicitly represented, and the knowledge
  itself can be examined and manipulated
• Symbol-less approach
• An approach that is modeled after the brain

24
Neural Networks

• This technique defines the connectionism camp of
  artificial intelligence
• More focus on how human or natural intelligence
  occurs
• Humans have neural networks, consisting of
  hundreds of billions of brain cells called
  neurons
• Neurons are connected by adaptive synapses which
  act as switching systems between the neurons
• Artificial neural networks
• These are based on the massively parallel
  architecture found in the brain
• They process information by processing large
  amounts of raw data in a parallel manner

25
Neuron
Neuron
Neuron
Switching System (Adaptive Synapses)
Neuron
Neuron
Neuron
Neuron
Neuron
Neuron
26
Neural Networks, contd.

• Operations of neural networks
• Different formulations of neural networks are
  used to
• Segment or cluster data, classify data, make
  predictive models using data
• A collection of processing units which mimic the
  basic operations of real neurons is used to
  perform these functions
• Learning or training
• As the neural network learns or is trained, a set
  of connection weights between the processing
  units is modified based on the perceived
  relationship in the data

27
Learning in Neural Networks
Processing Unit (Collection of Neurons)
Connection Weight
Processing Unit (Collection of Neurons)
Processing Unit (Collection of Neurons)
Connection Weight
Processing Unit (Collection of Neurons)
Processing Unit (Collection of Neurons)
Connection Weight
28
Neural Network and Cognitive Functions

• Neural networks
• Compared to symbol processing systems, neural
  networks perform relatively low-level cognitive
  functions
• Knowledge gain through learning is stored in the
  connection weights and is not available for
  examination manipulation
• Adaptability
• The ability of neural networks to learn from and
  adapt to their surrounds is a crucial function
  needed by intelligent software systems
• Cognition
• From a cognitive science perspective, neural
  networks are more like the underlying pattern
  recognition and sensory processing that is
  performed by the unconscious levels of the human
  mind

29
The Internet and the WWW

• The Internet grew out of government funding for
  high energy physics researchers who needed to
  collaborate over great distances
• Byproduct of solving the communication problem
• Developed protocols that allows different
  computers to talk to each other, exchange data,
  and work together
• TCP/IP became the de facto standard networking
  protocol for the Internet
• Astounding Growth in the Internet
• Exponential growth in the number of sites
• Thousands of new sites are connected to the
  Internet each month

30
The Internet and the WWW, contd.

• Internet Services
• Electronic mail was once the primary service
  provided by the Net
• Information publishing and software distribution
  are now of equal importance
• The Gopher text information service early 1990s
• Gopher was the information publishing on the Net
• FTP provides valuable services
• Download research papers and articles, retrieve
  software updates, and download complete software
  products
• It was HTTP that brought the Internet from the
  realm of academia and computer technologists into
  the public consciousness

31
The Internet and the WWW

• Mosaic browser University of Illinois
• Transformed the Internet into a general-purpose
  communication medium
• Computer novices and experts, consumers, and
  businesses interact in entirely new ways
• The Net has become a new business platform
• Web Services
• The Web publishing and broadcasting capabilities
  has extended the range of applications and
  services
• VoD, streaming audio and video, etc
• The ubiquitous Web browser provides a universal
  interface to applications regardless of server
  platform
• In the browsing or pull mode, the Web allows
  individual to explore vast amounts of data in a
  seamless environment

32
Web Services

• Limitations of the Browsing or Pull model
• The basic problem is that knowing that all the
  information is out there but not knowing exactly
  how to find it
• This can make the Web browsing experience quite
  frustrating
• Search engines
• Search engines and Web index sites such as Alta
  Vista, Excite, Yahoo, and Lycos provide important
  services by grouping information by topics and
  keywords
• Web browsing is still a hit or miss proposition
  (with misses more likely than hits)

33
Web Services, contd.

• Intelligent Agents
• In the current Web environment, intelligent
  agents will emerge as truly useful personal
  assistants
• Perform tasks such as searching, finding, and
  filtering information from the Web, and bringing
  it to a users attention
• The Evolving Web
• The Web is evolving into a push or broadcast
  mode, where users subscribe to sites which send
  out constant updates to their Web pages
• In the broadcast mode, the requirement for
  filtering information will not go away
• Unless the broadcast sites are able to send out
  personalized streams of information

34
Intelligent Agent

• Part-III

35
From AI to Intelligent Agents

• Whenever a technical field provokes commercial
  interest, this normally results in intense
  inertia towards market positioning
• AI and Commercial Interest
• The same is true for the AI community
• There has been a large movement and change of
  focus in the AI research community to apply the
  basic artificial intelligence techniques to a
  host of commercial interest
• Distributed computer systems, company wide
  intranets, the Internet, and the WWW
• Early focus was on word searches, information
  retrieval, and filtering tasks

36
From AI to Intelligent Agents, contd.

• Intelligent Agents and Commercial Interest
• Web in evolving into a collaborative commerce
  (c-commerce) environment transactions are
  becoming increasing distributed in nature
• There significant interest in having smart agents
  which can perform specific actions
• Many researchers have turned their focus to
  looking at how intelligent agents could cooperate
  to achieve tasks on distributed computer systems
• There is finally a problem in search of a
  technology
• As opposed to the other way around
• Intelligent Agents can provide real value to
  users in this new, interconnected, and networked
  world

37
Summary

• Abstract look at software agents
• We have discussed artificial intelligence and its
  evolution into software agents at an abstract
  level
• We will now take a brief tour of
• The technical facets of intelligent agents
• How they work
• How we classify them based on their abilities and
  underlying technologies

38
Event-Condition-Action

• Scenario
• Suppose we have an intelligent agent, running
  autonomously, primed with knowledge about the
  tasks we required of it.
• The agent is ready to move out on the network
  when the opportunity arises.
• Now what?
• How does the agent know that we want it to do
  something for us, or that it should respond to
  someone who is trying to contact us?
• This is where we have to deal with events,
  recognize conditions, and take actions

39
Event-Condition-Action
Agent
If (event1,event2condition) Then Action1
Action2
40
Event

• Events
• An event is anything that happens to change the
  environment of which the agent should be aware
• Arrival of a new piece of mail, change to a Web
  page, a timer going off at mid-night
• Would like to have asynchronous notification of
  events
• Agent would not have to be engaged in busy wait
  or polling for events
• Agents can sleep, think about what has happened
  during the day, do house keeping tasks, etc,
  while waiting for the next event
• Event notification
• When an event occur, the agent has to recognize
  and evaluate what the event means an then respond
  to it

41
Condition-Action

• Condition/Recognition
• Determining what the condition or state of the
  world is, could be simple or extremely complex
  depending on the situation
• New mail is a self-describing event
• The agent may then query the mail system to find
  out who sent the mail, what the subject is, or
  scan the mail text for keywords
• All of this is part of the recognition phase
• The initial event may wake up the agent, but the
  agent has to determine the significance of the
  event in terms of its duties

42
Condition-Action

• Condition/Recognition/Action
• If intelligent Agents are going to make our lives
  easier or more interesting, they must be able to
  take action, to do things for us
• Having an agent take an action for us requires a
  certain leap of fait or at least some level of
  trust
• We must trust that our intelligent agent is going
  to behave rationally and in our best interest
• Like all situations where we delegate
  responsibility, we have to weigh the risks and
  rewards
• Risk agent could mess things up, more work to
  get it right
• Reward we are free from performing that piece of
  work

43
Processing Strategies

• Reactive or reflex agents
• These are one on the simplest types of agents.
  They respond in the event-condition-action mode
• Reflex agents do not have internal models of the
  world
• They respond solely to external stimuli and the
  information available from their sensing of the
  environment
• Like neural networks, reactive agents exhibit
  emergent behavior interactions of simple
  individual agents
• Reactive agents share low-level data when they
  interact, not high-level symbolic knowledge
• Reactive agents are grounded in physical sensor
  data and not at the artificial symbolic space
• Applications of reactive agents have been limited
  to robots which use sensors to perceive the world

44
Processing Strategies

• Deliberative or goal-directed agents
• These agents have domain knowledge and the
  planning capability necessary to take a sequence
  of actions in the hope of reaching or achieving a
  specific goal
• Deliberative agents may proactively cooperate
  with other agents to achieve a task
• They may use any and all of the symbolic
  artificial intelligence techniques which have
  been developed over the past forty years

45
Processing Strategies

• Collaborative agents
• These agents work together to solve problems
• Communication between agents is an extremely
  important element
• Each individual agent is autonomous
• The synergy resulting from their cooperation
  makes them interesting and useful
• These agents can solve large problems which are
  beyond the scope of any single agent and they
  allow a modular approach based on specialization
  of agent functions or domain knowledge.
• Complex engineering projects verify different
  aspects of the design.