Title: EAI / 02_agents

Q: What does an agent function do?
A: the agent function maps percept histories, i.e. sequences of percepts to actions

Q: What does a rational agent do?
A: For each possible percept history, select an action that is expected to maximize its performance measure.

Q: Name the criteria of environment.
A: - fully observable vs. partially observable
- deterministic vs. nondeterministic
- episodic vs. sequential
- static vs. dynamic (does the world change during the reasoning time of the agent)
- discrete vs. continuous
- known vs. unknown
- single-agent vs. multi-agent

Q: Name the types of agents in order of increasing generality.
A: 1. simple reflex agents
2. reflex agents with state (model-based reflex agent)
3. goal-based agents
4. utility-based agents

Q: Explain the simple reflex agent.
A: examples: avoid collision, maintain temperature
- no memory
- no sequences of percepts
- looping possible: `if closed(door) then open_door`

Q: Explain the model-based agent.
A: add-on: internal state and model of the world's evolution
- memory, maintain/update world state
- reason about unobservable parts
- deal with uncertainty, implicit goals

Q: Explain the goal-based agent.
A: add-on: explicit goals
- model the world, goals, and actions & their effects explicitly
- more flexible, better maintainable
- search & planning for single/sequence of action/s to achieve a goal (e.g. get from A to B)

Q: Explain the utility-based agent.
A: add-on: take happiness into account
- assess goals with a utility function
- resolve conflicting goals
- use expected utility for decision

Q: What makes an agent "intelligent"?
A: Acting rationally.