Docsity
Docsity
Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity

Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan

Guidelines and tips
Guidelines and tips
Sell on Docsity
Sell on Docsity
Docsity AI
Docsity AI
Log inSign up

Prepare for your exams

Study with the several resources on Docsity

Find documents

Prepare for your exams with the study notes shared by other students like you on Docsity

Search for your university

Find the specific documents for your university's exams

Docsity AINEW

Summarize your documents, ask them questions, convert them into quizzes and concept maps

Explore questions

Clear up your doubts by reading the answers to questions asked by your fellow students

Earn points to download

Earn points by helping other students or get them with a premium plan

Share documents
download point icon20 Points

For each uploaded document

Answer questions
download point icon5 Points

For each given answer (max 1 per day)

All the ways to get free points
Get points immediately

Choose a premium plan with all the points you need

Study Opportunities

Choose your next study program

Get in touch with the best universities in the world. Search through thousands of universities and official partners

Community

Ask the community

Ask the community for help and clear up your study doubts

Free resources

Our save-the-student-ebooks!

Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors

CPSC 420 Midterm Review: Artificial Intelligence and Search Algorithms - Prof. Y. Choe, Study notes of Computer Science

Texas A&M University (A&M)Computer Science
Prof. Y. Choe

A comprehensive review of the topics covered in the cpsc 420 artificial intelligence course, focusing on ai basics, search algorithms, game playing, propositional logic, and first-order logic. Students are encouraged to understand the concepts rather than memorizing them, and various resources are suggested for further practice.

Typology: Study notes

Pre 2010

Uploaded on 02/13/2009

koofers-user-qko
koofers-user-qko 🇺🇸

5

(4)

10 documents

1 / 4

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
CPSC 420 Midterm Review: Overview
•AI basics
•Search as a problem solving strategy
•Game playing
•Propositional logic
•No Lisp questions.
1
AI Basics
•Disciplines with ties to AI: think about how they did and would
contribute
•What are the hard problems in AI? Why are they hard?
•Just read over the slides so that you have the general idea.
2
Uninformed Search
•Description of a search problem: initial state, goals, operators,
etc.
•Considerations in designing a representation for a state
•Evaluation criteria
•BFS, DFS: time and space complexity, completeness
•When to use one vs. another
•Node visit orders for each strategy
•Tracking the stack or queue at any moment
3
Uninformed Search / Informed Search
•DLS, IDS, BDS search order, expansions, and queueing
•DLS, IDS, BDS evaluation
•DLS, IDS, BDS: suitable domains
•Repeated states: why removing them is important
•Constraint Satisfaction Search: what kind of domains? why
important?
4
pf3
pf4

Partial preview of the text

Download CPSC 420 Midterm Review: Artificial Intelligence and Search Algorithms - Prof. Y. Choe and more Study notes Computer Science in PDF only on Docsity!

CPSC 420 Midterm Review: Overview

  • AI basics
  • Search as a problem solving strategy
  • Game playing
  • Propositional logic
  • No Lisp questions. 1 AI Basics
  • Disciplines with ties to AI: think about how they did and would contribute
  • What are the hard problems in AI? Why are they hard?
  • Just read over the slides so that you have the general idea. 2 Uninformed Search
  • Description of a search problem: initial state, goals, operators, etc.
  • Considerations in designing a representation for a state
  • Evaluation criteria
  • BFS, DFS: time and space complexity, completeness
  • When to use one vs. another
  • Node visit orders for each strategy
  • Tracking the stack or queue at any moment Uninformed Search / Informed Search
  • DLS, IDS, BDS search order, expansions, and queueing
  • DLS, IDS, BDS evaluation
  • DLS, IDS, BDS: suitable domains
  • Repeated states: why removing them is important
  • Constraint Satisfaction Search: what kind of domains? why important?

Informed Search

  • Best-first-search: definition
  • Heuristic function h(n): what it is
  • Greedy search: relation to h(n) and evaluation. How it is different from DFS (time complexity, space complexity).
  • Difference between heuristic search (or hill-climbing) and greedy search.
  • A∗: definition, evaluation, conditions of optimality
  • Complexity of A∗: relation to error in heuristics
  • Designing good (admissible) heuristics: several rule-of-thumbs 5 Informed Search: Iterative Improvement Algorithms - IDA∗: evaluation, time and space complexity (worst case) - What is a dominant heuristic and why is it better? - Hill-climbing basics and strategies - Beam search concept - Simulated annealing details: core algorithm, effect of T and ∆E, source of randomness. 6 Game Playing
  • Game playing: what are the types of games?
  • Minimax: definition, and how to get minmax values
  • Minimax: evaluation

α − β Pruning

  • α − β pruning: the algorithm, rationale, and why it saves time
  • α − β pruning algorithm: know how to apply pruning
  • α − β pruning properties: evaluation
  • Games with an element of chance: what are the added elements? how does the minmax tree get augmented?

Logic: Resolution Applies to both propositional logic and first-order logic.

  • Given a theorm to prove, know how to convert it into a form suitable for resolution.
  • Know how to do resolution in propositional logic
  • Know how to do resolution in first-order logic General Rule-of-Thumb
  • Get used to basic concepts (representation of search problems, game playing basics, propositional and first-order logic constructs and rules)
  • Try not to blindly memorize what’s there , try to understand why/how something works or does not work : - example: time and space complexity of search strategies, alpha-beta pruning.
  • Try out the exercises done in the class, and the homework.
  • Try out exercises in the textbook related to the material presented in the lectures.
  • Try out the past exam.
  • Laws of logic will be provided with the exam.