CS 480 Exam One Terms and Concepts
Chapter 1
- What is AI?
- thinking vs acting, humanly vs rationally
- a little bit of history
Chapter 2 Intelligent Agents
- Terms: agent, environment, sensors, actuators, percept, agent
function, agent program, rational agent, performance measure,
information gathering, learning
- Describing task environments (PEAS)
- Properties of task environments:
- fully observable vs partially observable
- single agent vs multiagent
- deterministic vs stochastic
- episodic vs sequential
- static vs dynamic
- discrete vs continuous
- kinds of agent programs:
- table-driven agents
- simple reflex agents
- model-based reflex agents
- goal-based agents
- utility-based agents
- learning agents
- representational complexity: atomic, factored, structured
Chapter 3 Search
- problem-solving agents
- formulating problems, consisting of:
- initial state
- actions
- transition model
- goal test
- path cost function
- basic tree search and graph search algorithms
- measuring performance:
- completeness
- optimality
- time complexity
- space complexity
- Uninformed search strategies:
- breadth-first search
- uniform-cost search
- depth-first search
- depth-limited search
- iterative deepening depth-first search
- Informed search strategies:
- greedy best-first search
- A* search
- what heuristics are, where they come from, what makes a
heuristic admissible
Chapter 4 Local Search and Optimization
- hill climbing search
- simulated annealing
- genetic algorithms
Chapter 5 Adversarial Search
- basic organization of adversarial search: initial state,
transition model, terminal test, utility function
- minimax algorithm
- minimax with alphabeta pruning
- effect of adding chance to adversarial search
Chapter 6 Constraint Satisfaction Problems
- Basic idea that a CSP consists of a set of variables, domains
for each variable and a set of constraints on the values of the
variables.
- Basic idea of the backtracking search algorithm.
- Some notion of the heuristics for doing effective search.
- Idea of using local search (on complete problem instances) to
solve CSP problems