1 / 8

CS460 Spring 2011

CS460 Spring 2011. Review. Overview. Course overview Propositional Logic Example CSP Example Hints for Final. Course Review. AI introduction Agents Searching Uninformed Informed Local Adversarial search Constraint satisfaction problems Propositional Logic Looking ahead.

noel
Télécharger la présentation

CS460 Spring 2011

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CS460Spring 2011 Review

  2. Overview • Course overview • Propositional Logic Example • CSP Example • Hints for Final

  3. Course Review • AI introduction • Agents • Searching • Uninformed • Informed • Local • Adversarial search • Constraint satisfaction problems • Propositional Logic • Looking ahead

  4. Propositional Logic Example • 7.22 (homework problem), minesweeper

  5. C. For each cell that has been probed, take the resulting number n revealed by the game and construct a sentence with 8 choose ndisjuncts. Conjoin all the sentences together. Then use DPLL to answer if this sentence entails X[I,j]for the I,j needed. d. To encode the global constraint that there are M mines altogether, we can construct a disjunct with N choose M disjuncts, each of size N. For N=100 and M=20, this works out to over 10**39, which is too large for any computer. However, we can represent the global constraint within the DPLL algorithm itself, by adding min and max parameters to the DPLL function, to represent the minimum and maximum symbols that must be true in the model. For an unconstrained problem, min=0 and max=N. For the minesweeper, we can use M for both min and max. Modify DPLL to fail if min< remaining or max <0. Run DPLL recursively by subtracting 1 from both min and max when true is assigned to a symbol.

  6. CSP Example • Download and install JaCoP Java Constraint Programming package. • Look at examples • Zebra problem (6.7 in textbook)

  7. Hints for Final • Review and make corrections on all the midterm problems • Review all homeworks • Review the posted ppts / pdfs on the wiki page • Review the midterm review and final review sessions • Review the textbook sections corresponding to the class notes • Review aimacode packages that you used in homeworks and saw in class. • Do selected problems at the back of the chapters in the book.

More Related