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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.
Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net

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AI: Planning and AI

  • 2. Acting Its a process in which planning systems must face up to the awful prospect of actually having to take their own advice.
  • 3. 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.
  • 4. 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.
  • 5. 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.
  • 6. 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
  • 7. 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.
  • 8. 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)
  • 9. 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.
  • 10. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net