No questions involving precise dates or fields of AI and AI History,
but you should have an idea of where AI has come from and why. Our
discussion of intelligence, rationality and the Turing test helps
formulate the basis for most of our problem solving.


You should be able to discuss PEAS for a particular problem as well as
what kinds of environments that problem involves. Understanding
different agent types and how they are needed as steps to intelligence
(or perhaps pieces of intelligence) is important.

Most questions (>50%) on this exam will come from Chapter 3.  Be sure
to understand how search strategies expand nodes and which strategy to
use for what types of problems. Having an understanding of optimality,
completeness and time/space complexity is necessary in order to make
this distinction. Given a problem and being able to choose the proper
search strategy is a skill you should develop when preparing for this
test. Understanding the role of heuristics in informed search is also
very important. A* is one of the cornerstone algorithms of AI, so be
sure you understand it completely.



CSI 460 

Chapter 1
1. Fields that influenced AI  (p 5-16)
2. History of AI (p 16-28)
3. Thinking, Acting, Rationality, Turing Test (p1-5)

Chapter 2
1. Agents, percepts, actions, sensors, environments (p34-39)
2. PEAS and environment types (p40-46)
3. Agent Types (p46-58)

Chapter 3
1. Problem solving (p64-76)
2. Tree-Search, Child node (p77-79)
3. Measuring performance: completeness, optimality, time, space (p80)
4. Strategies: BFS, Uniform-cost, DFS, DLS, IDS (81-91)
5. Informed Strategies: Greedy, A* (92-99)
6. Heuristics, admissible, consistent, dominance, relaxed problems (p102-107)


Local Search Algorithms
  - iterative improvement - constant space
  - hill climbing and options, random restarts, random sideways
  - simulated annealing
  - local beam search
  - genetic algorithms
  - continuous state spaces, discretization, gradients, Newton-Rhapson iterations

Constraint Satisfaction Problems
  - Map - coloring
  - constraint graph - directed and undirected
  - varities and examples
  - incremental search
  - backtracking search (DFS)
  - minimum remaining values, degree heuristic, least constraining value, forward checking , constraint propagation/arc consistency