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AI: Planning and AI

AI: Planning and AI

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AI: Planning and AI AI: Planning and AI Presentation Transcript

  • Planning and AI
  • Acting
    Its a process in which planning systems must face up to the awful prospect of actually having to take their own advice.
  • Conditional planning
    Also known as contingency planning.
    Conditional planning deals with incomplete information by constructing a conditional plan that accounts for each possible situation or contingency that could arise.
    The agent finds out which part of the plan to execute by including sensing actions in the plan to test for the appropriate conditions.
  • The nature of conditional plans
    The condition must be known to the agent at that point in the plan.
    To ensure that a conditional plan is executable, the agent must insert actions that cause the relevant conditions to become known by the agent.
  • What is a Situated planning Agent?
    Rather than thinking of Agent as the planner which passes its results to execution monitor as separate processes,
    We can think of them as a single process in a situated planning agent.
  • Functions in situated planning agent algorithm
    Static
    Termination
    Resolving standard flaws
    Remove unsupported causal links
    Extend causal links back to earliest possible step
    Remove redundant actions
    Execute actions when ready for execution
  • Acting Under Uncertainty
    The presence of uncertainty changes radically the way in which an agent makes decisions.
    To make such choices, an agent must first have preferences between the different possible outcomes of the various plans , utility theory can be used to represent and reason with preferences.
  • The Axioms of Probability
    All probabilities are between 0 and 1.0 < P(A) < 1
    Necessarily true propositions have probability 1, and necessarily false propositions have probability 0.P(True) = 1 P(False) = 0
     The probability of a disjunction is given byP(A V B) = P(A) + P(B) - P(A / B)
  • The joint probability distribution
    A probabilistic model of a domain consists of a set of random variables that can take on particular values with certain probabilities.
    Let the variables be X1 ... Xn.
    An atomic event is an assignment of particular values to all the variables—in other words, a complete specification of the state of the domain.
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