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Title: CS451CS551EE565 ARTIFICIAL INTELLIGENCE


1
CS451/CS551/EE565ARTIFICIAL INTELLIGENCE
  • Search
  • 9-4-2006
  • Prof. Janice T. Searleman
  • jets_at_clarkson.edu, jetsza

2
Outline
  • Other Intelligent Agents
  • mobile
  • information
  • Search
  • HW2 posted due Wednesday, 9/6/06

3
Mobile agents
  • Programs that can migrate from one machine to
    another.
  • Execute in a platform-independent execution
    environment.
  • Require agent execution environment (places).
  • Mobility not necessary or sufficient condition
    for agenthood.
  • Practical but non-functional advantages
  • Reduced communication cost (eg, from PDA)
  • Asynchronous computing (when you are not
    connected)
  • Two types
  • One-hop mobile agents (migrate to one other
    place)
  • Multi-hop mobile agents (roam the network from
    place to place)
  • Applications
  • Distributed information retrieval.
  • Telecommunication network routing.

4
Mobile agents
  • Programs that can migrate from one machine to
    another.
  • Execute in a platform-independent execution
    environment.
  • Require agent execution environment (places).
  • Mobility not necessary or sufficient condition
    for agenthood.

A mail agent
5
Mobile agents
  • Practical but non-functional advantages
  • Reduced communication cost (e.g. from PDA)
  • Asynchronous computing (when you are not
    connected)
  • Two types
  • One-hop mobile agents (migrate to one other
    place)
  • Multi-hop mobile agents (roam the network from
    place to place)

6
Mobile agents
  • Applications
  • Distributed information retrieval.
  • Telecommunication network routing.

7
Information agents
  • Manage the explosive
  • growth of information.
  • Manipulate or collate information
  • from many distributed sources.
  • Information agents can be mobile or static.
  • Examples
  • BargainFinder comparison shops among Internet
    stores for CDs
  • Internet Softbot infers which internet facilities
    (finger, ftp, gopher) to use and when from
    high-level search requests.
  • Challenge ontologies for annotating Web pages
    (eg, OWL).

8
How is an Agent different from other software?
  • Agents are autonomous, that is, they act on
    behalf of the user
  • Agents contain some level of intelligence, from
    fixed rules to learning engines that allow them
    to adapt to changes in the environment
  • Agents don't only act reactively, but sometimes
    also proactively

9
How is an Agent different from other software?
  • Agents have social ability, that is, they
    communicate with the user, the system, and other
    agents as required
  • Agents may also cooperate with other agents to
    carry out more complex tasks than they themselves
    can handle
  • Agents may migrate from one system to another to
    access remote resources or even to meet other
    agents

10
Summary Agents
  • Intelligent Agents
  • Anything that can be viewed as perceiving its
    environment through sensors and acting upon that
    environment through its effectors to maximize
    progress towards its goals.
  • PAGE (Percepts, Actions, Goals, Environment)
  • Described as a Perception (sequence) to Action
    Mapping f P ? A
  • Using look-up-table, closed form, etc.
  • Agent Types reflex, state, goal-based,
    utility-based
  • Rational Action The action that maximizes the
    expected value of the performance measure given
    the percept sequence to date

11
Solving Problems by Searching
12
Outline
  • Problem-solving agents
  • Problem types
  • Problem formulation
  • Example problems
  • Basic search algorithms

13
What is a problem?
  • want to achieve a goal, but there is no direct
    way to do it

14
Problems???
  • In the middle of taking the GREs, your only 2
    pencil breaks
  • 15-puzzle
  • Instant Insanity build a tower of 4 blocks so
    that each of the 4 colors shows on each of the 4
    sides of the tower
  • Typographical Rebus (wacky wordies)
  • Monk on the Mountain

15
Typographical Rebuses
16
Monk on the Mountain
  • One day an old monk decides to leave his
    monastery at precisely 600 am to climb to the
    top of a mountain so he can enjoy its solitude.
    He travels at various speeds and takes several
    rests before arriving at the mountain peak at
    precisely 500 pm. He spends the night in prayer
    and meditation, and starts back down the mountain
    using the same trail the next day at precisely
    600 am, again traveling at different speeds and
    stopping often for periods of rest. At precisely
    500 pm he reaches his starting point back at the
    monastery. Is there some point along the
    mountain trail that he passes at precisely the
    same time each day?

17
Steps for Problem Solving
  • Goal formulation what is desired
  • - may have a set of constraints or measures that
    should be optimized
  • - can specify a goal by describing a state in
    which it is achieved, or by giving a goal-test
    function
  • Problem formulation what actions states to
    consider
  • Search algorithm takes a problem as input
    returns a solution in the form of a sequence of
    actions to take
  • Execution carry out the actions

18
Problem Formulation
  • Representation is a key issue in problem solving.
    How a problem is formulated determines what
    means can be brought to bear to solve the problem
    (and possibly whether or not it can be solved).
  • For example, consider designing the
    cross-sectional shape of an airplane wing.
    Possibilities include
  • model the wing using mathematical techniques
  • build a mockup of the wing and test it in a wind
    tunnel
  • create a model in virtual reality
  • etc.

19
Representation Problem Formulation
Model
  • A representation is not just a way of encoding
    the knowledge of a problem
  • it determines what means are available for
    working on the problem
  • it determines how expensive processing is
  • it may determine whether or not all relevant
    knowledge can be encoded

20
AI Technique vs. Algorithms
  • algorithm to solve the problem may be known, but
    is intractible (e.g. chess)
  • no algorithm is known (e.g. playing ping pong)
  • algorithms assume complete and consistent data

21
Problem-solving agents
22
Example Romania
  • On holiday in Romania currently in Arad.
  • Flight leaves tomorrow from Bucharest
  • Formulate goal
  • be in Bucharest
  • Formulate problem
  • states various cities
  • actions drive between cities
  • Find solution
  • sequence of cities, e.g., Arad, Sibiu, Fagaras,
    Bucharest

23
Example Romania
24
Problem types
  • Deterministic, fully observable ? single-state
    problem
  • Agent knows exactly which state it will be in
    solution is a sequence
  • Non-observable ? sensorless problem (conformant
    problem)
  • Agent may have no idea where it is solution is a
    sequence
  • Nondeterministic and/or partially observable ?
    contingency problem
  • percepts provide new information about current
    state
  • often interleave search, execution
  • Unknown state space ? exploration problem

25
Example vacuum world
  • Single-state, start in 5. Solution?

26
Example vacuum world
  • Single-state, start in 5. Solution? Right,
    Suck
  • Sensorless, start in 1,2,3,4,5,6,7,8 e.g.,
    Right goes to 2,4,6,8 Solution?

27
Example vacuum world
  • Sensorless, start in 1,2,3,4,5,6,7,8 e.g.,
    Right goes to 2,4,6,8 Solution?
    Right,Suck,Left,Suck
  • Contingency
  • Nondeterministic Suck may dirty a clean carpet
  • Partially observable location, dirt at current
    location.
  • Percept L, Clean, i.e., start in 5 or
    7Solution?

28
Example vacuum world
  • Sensorless, start in 1,2,3,4,5,6,7,8 e.g.,
    Right goes to 2,4,6,8 Solution?
    Right,Suck,Left,Suck
  • Contingency
  • Nondeterministic Suck may dirty a clean carpet
  • Partially observable location, dirt at current
    location.
  • Percept L, Clean, i.e., start in 5 or
    7Solution? Right, if dirt then Suck

29
Single-state problem formulation
  • A problem is defined by four items
  • initial state e.g., "at Arad"
  • actions or successor function S(x) set of
    actionstate pairs
  • e.g., S(Arad) ltArad ? Zerind, Zerindgt,
  • goal test, can be
  • explicit, e.g., x "at Bucharest"
  • implicit, e.g., Checkmate(x)
  • path cost (additive)
  • e.g., sum of distances, number of actions
    executed, etc.
  • c(x,a,y) is the step cost, assumed to be 0
  • A solution is a sequence of actions leading from
    the initial state to a goal state
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