1. Intelligent Agents
R & N Chapter 2

2. Rationality
“A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.”
What does “best” mean?
What’s the outcome?
How much does it cost to get it?
How can we compute the best expected outcome?
Bounded rationality (satisficing search)
Best: someone, usually outside the agent, has to decide

3. PEAS Descriptions and State Space Search
PEAS: What’s observable in the world **Given by the task
Performance Measure
Environment
Actuators
Sensors
State space search: Internal reasoning **Created by designer
States
Initial state
Goal state(s)
Successor function
Costs (of paths and/or states)

4. How Do They Relate? Criminal defense lawyer Perry Mason style:
PEAS: What’s observable in the world
Performance Measure
Environment
Actuators
Sensors
State space search: Internal reasoning
States
Initial state
Goal state(s)
Successor function
Costs (of paths and/or states)
Environment: arrests, warrants, cops doing things, evidence collected
Actuators: request warrents, interview witnesses, collect data
States: models (usually incomplete) for what happened
Successor function: inference engine that strings known facts together + ops corresponding to sensors
But note that a lot of internal reasoning may happen in between externally visible actions performed by actuators

5. How Do They Relate? Driving to California:
PEAS: What’s observable in the world
Performance Measure
Environment
Actuators
Sensors
State space search: Internal reasoning
States
Initial state
Goal state(s)
Successor function
Costs (of paths and/or states)
Environment: roads, places
Actuators:Turn the wheel, accelerate, brake
States: models (usually incomplete) for what happened. Can be much higher level. Must ignore things that
can go wrong, like detours, since info not available at planning time.
Successor function: go from one location to another. This time, note that there are a lot more physical actions than there are planning steps.

6. PEAS Examples Perf Envir Acts Sensors Measure Robot soccer player
Internet book-shopping agent
Autonomous Mars rover
Mathematicians’s assistant
Ask Jeeves
School lunch planning

7. Performance Measures Two kinds of considerations: Where do we end up?
How much does it cost (in time, gas, or whatever) to get there?
Sometimes only one of these matters, sometimes they both do.
Examples:
Just 1 matters:
Just 2 matters:
Both 1 and 2 matter:
1: chess
2: Mars Rover: learn as much as possible as cheaply as possible
3: Defense lawyer

8. Is the Solution a Path or a State?
Sometimes the solution is a state:
But sometimes we need to output the best path that we found:
State: find a consistent interpretation for a sentence like, “the bank president ate a dish of pasta salad with a fork”
book shopping agent
school lunch planner
Path: Route finding
theorem proving

9. Characterizing the Environment
Obs Det Epi Static Disc Agents Complexity
Soccer player
Defense lawyer
Ask Jeeves
Episodic: maybe no at lower levels but yes at higher. For lawyer, no within a case, yes across cases.
Static vs. dynamic
Discrete vs continuous applied to state, time, percepts or actions
Complexity – not in book. How regular or not are the connections between states? Another way to look at this? How much knowledge do you have to have about the domain?