## CS 827 - ARTIFICIAL INTELLIGENCE

### Meeting 1: Wed Feb 4 15:30:09 EST 2015

• class website: http://csis.pace.edu/~benjamin/teaching/cs827/

• Qualifying exam is not optional (if you completed exams requirement, it will just determine your course grade):

• Problems of the qualifying exam will be similar to those of the assignments.
• There will be Bayes rule in the exam.
• For the complete list of exam topics that you will be HELD RESPONSIBLE for, see: http://csis.pace.edu/~benjamin/teaching/cs827/topics.html
• Meeting schedule:
• 3 more flexible and upon request meetings.
• online video meetings is an option too.
• Homework submission:
• by email (anytime).
• Overview on type of problems (and algorithms) in AI:

• In general, AI deals with ill-structured problems.
• Search algorithms (initial state --> describe the problem --> explore the possible solutions 'usually exponential' ).
• DFS, BFS, A*, iterative depening ... etc.
• You are expected to KNOW these basic algorithms in the Qualifying Exam.

### Meeting 2: Wed Mar 4 15:37:42 EST 2015

Describing search algorithms:

Global Search algorithms

• DFS:

• backtracking (conceptually or metaphor), but in reality it's implemented using datastructure not really backtrack.
• Not complete, but more space efficient (linear space)
• BFS:

• Complete, but bad space complexity (exponential)
• Iterative deepining:

• Combining DFS and BFS.
• Will be on the exam.

How to determine which approach to use?

• Heuristic approach is a guess (e.g. how far you are from your goal) based on background knowledge about the problem domain.

• Greedy approach employees the heuristic approach (using evaluation function).

Hint: all course units (except 5 and 6) are worked out with examples at: http://csis.pace.edu/~benjamin/teaching/cs627

• A* algorithm:
• the most used search algorithm in AI.

Games algorihtms

• Minimax:
• see: http://csis.pace.edu/~benjamin/teaching/cs627/webfiles/games/games3.html

Local Search Algorithm

• Random-Restart (recent and popular) algorithm (you must read it).
• Easy to implement and less cost.
1. start at a randomly selected spot
2. search locally (tune) if no result: terminates and go to 1

### Meeting 3: Wed Mar 25 15:40:53 EDT 2015

Constraint Satisfaction

Reviewing Constraint Satisfaction Problems:

``````- ex: place n queen in NxN chess board with no queen attacking the other.
- See: Example: Cryptarithmetic (chapter5.pdf page.9) and http://csis.pace.edu/~benjamin/teaching/cs627/webfiles/csp/csp.15b.html
- See: http://csis.pace.edu/~benjamin/teaching/cs627/webfiles/csp/csp.1.html``````

Logic

• Propositional Logic:
• expressing facts.
• Syllogism:
• symbolic way to conclude facts.
• Predicate logic:
• boolean value functions.
• e.g. sunny(Friday) = True

vacuously true.

See exercise 8.9:

``- in exam , we will have similar questions.``

### Meeting 4: Wed Apr 15 15:36:15 EDT 2015

This is the Final Meeting of this class

Qualifying exam preminlinary date: Wed. May, 27 2015