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Intelligent Agents


... on the Web, with a Java twist. 3. Artificial Intelligence: Introduction ... Math related skills: proving theorems, geometry, calculus, games (checkers, chess) ... – PowerPoint PPT presentation

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Title: Intelligent Agents

Intelligent Agents
  • With Java

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

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
  • Game playing, mathematical theorem proving,
    common-sense reasoning, college mathematics

Introduction, contd.
  • AI research labs began specializing in narrow
  • 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
  • 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

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
  • Considered highly controversial and impractical
    when first introduce by the IA community
  • Object-oriented programming techniques
  • Refinement of the AI Frames concept

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
  • 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

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

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)

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
  • Expert systems workstations with powerful
    integrated development environments were
  • Lisp, Prolog, Smalltalk
  • These were years ahead of commercial software

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

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

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

Foot Note
  • Using commercial success as a measure of the
    value of technology is problematic to say the
  • 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
  • Source is abundant (water for instance)
  • The ability to transform and make readily
    available is attainable by every society
  • Low technological barrier

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
  • 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

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

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

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

Intelligent Agents
  • Part-II

Intelligent Behavior
  • There are many behaviors to which we ascribe
  • 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
  • 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

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

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,
  • History has shown that symbols may be appropriate
    for reasoning and planning
  • Pattern recognition and learning are suited for
    other approaches

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
  • Frames similar to Objects in the OO paradigm
  • Attributes of a concept are grouped together with
    related procedures for processing

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

Neural Networks
  • This technique defines the connectionism camp of
    artificial intelligence
  • More focus on how human or natural intelligence
  • Humans have neural networks, consisting of
    hundreds of billions of brain cells called
  • 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

Switching System (Adaptive Synapses)
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

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
Neural Network and Cognitive Functions
  • Neural networks
  • Compared to symbol processing systems, neural
    networks perform relatively low-level cognitive
  • 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

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

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
  • It was HTTP that brought the Internet from the
    realm of academia and computer technologists into
    the public consciousness

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
  • VoD, streaming audio and video, etc
  • The ubiquitous Web browser provides a universal
    interface to applications regardless of server
  • In the browsing or pull mode, the Web allows
    individual to explore vast amounts of data in a
    seamless environment

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
  • 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
  • Web browsing is still a hit or miss proposition
    (with misses more likely than hits)

Web Services, contd.
  • Intelligent Agents
  • In the current Web environment, intelligent
    agents will emerge as truly useful personal
  • 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

Intelligent Agent
  • Part-III

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

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
  • As opposed to the other way around
  • Intelligent Agents can provide real value to
    users in this new, interconnected, and networked

  • Abstract look at software agents
  • We have discussed artificial intelligence and its
    evolution into software agents at an abstract
  • 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

  • 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

If (event1,event2condition) Then Action1
  • 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
  • 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

  • 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

  • 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
  • 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
  • Risk agent could mess things up, more work to
    get it right
  • Reward we are free from performing that piece of

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
  • They respond solely to external stimuli and the
    information available from their sensing of the
  • 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

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

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.