AI: Planning and AI


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AI: Planning and AI

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AI: Planning and AI

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