State Space Representations Intro
- real world is complex, state space must be abstracted for problem solving
- abstract states map to sets of real states
- abstract action maps to combination of real actions
- abstract solution = set of real paths that are solutions in real world
- there may be multiple different state space representations
Problem-solving agent uses this representation:
- what are actions to move between states?
- what are appropriate states & initial states?
- what is the cost of an action?
- goal: what are the successful world states?
- search: determine possible sequences of actions leading to goal, choose ‘best’ sequence
- execute: give solution, perform actions
State space representation (example - vacuum cleaner):
- start with real-life problem
- formulate abstract problem (states, actions)
- formulate concrete (clean house) & algorithmic goal (be in state 7 and 8)
- find solution (sequence of actions to get to state 7 or 8)
- execute plan (clean house according to abstract solution)