Diese Präsentation wurde erfolgreich gemeldet.
Die SlideShare-Präsentation wird heruntergeladen. ×

Lecture 26 local beam search

Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Wird geladen in …3
×

Hier ansehen

1 von 10 Anzeige
Anzeige

Weitere Verwandte Inhalte

Weitere von Hema Kashyap (20)

Aktuellste (20)

Anzeige

Lecture 26 local beam search

  1. 1. Local Beam Search Lecture-26 Hema Kashyap 1
  2. 2. Idea • The search begins with k randomly generated states • At each step, all the successors of all k states are generated • If any one of the successors is a goal, the algorithm halts • Otherwise, it selects the k best successors from the complete list and repeats • The parallel search of beam search leads quickly to abandoning unfruitful searches and moves its resources to where the most progress is being made • In stochastic beam search the maintained successor states are chosen with a probability based on their goodness 2
  3. 3. Algorithm 3
  4. 4. 4
  5. 5. 5
  6. 6. Assumptions • Assume a pre-fixed width i.e. 2 • Perform bredth-first, • But only keep the WIDTH best new nodes • Depending on heuristic at each new level 6
  7. 7. Example 7
  8. 8. Example 8
  9. 9. Example 9
  10. 10. Example 10

×