由于中间有很长一段时间在课上做ICS-PA去了，笔记中间断线了许多
虽然这课确实比较水没讲啥就是
Agent
Introduction
Agent: anything that can be viewed as perceiving its envivronment through sensors and acting upon that environment through actuators.
Rational agent: select an action to maximize given performance measure based on the percept sequence and built-in knowledge * Different from omniscience * Different from perfect
Designing agents: PEAS * Performance measure * Environment * Actuators * Sensors
Type of Environment
Fully observable: Everything the agent needs to make decision is accesible via its sensors. vs. partially observable, when not fully accesible, the agent needs to make informed guesses. In decision theory: perfect information vs. imperfect information.
Deterministic: agent does the change of world state. * The decision only depends on the agent decision and current state. vs. stochastic: the change of the world state beyond the control of agent (in some aspects). * Need to make guesses about the world change.
Episodic: current decision doesn’t depends on previous action. vs. sequential: current choice will affect future decision.
Static: the environment won’t change chronologically. vs. dynamic: the environment changes upon time. vs. semidynamic: the environment doesn’t change, but the agents performance score(e.g. the performance measure) will change with the passage of time.
Discrete: the percepts and actions is limited, dinstinct and clearly defined. vs. continuous: the percepts and actions is limited in a range.
Single agent: only one agent in the environment. vs. multiagent: multiple agent working together.
Type of Agent
Simple reflex agents percepts -> condtion-based action rules -> actions
- Actions only depend on current percept.
- Drawback: may develop deadloop.
Reflex agents with state/model
- Know how world evolves.
Goal-based agents
- Goal as the performance measure.
- Know what it will be like if it does some action.
Utility-based agents
- Consider the problem as Utilities beyond a simple goal.
- Learning agents
- All the four agents above can be seen as a learning agents(if they get the ability to learn).
- Has a problem generator, suggests exploring new actions when trying to solve new upcoming problems.
- State space graph: each state exists only once
- Seaarch Tree: same state can be in multiple nodes
Evaluating search algo:
- completeness
- time complexity -> can be evaluated by counting total nodes generated
- space complexity
- optimality