1. ARTIFICIAL INTELLIGENCE
CS 512
ARTIFICIAL INTELLIGENCE
LECTURE 02

2. Agents
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

3. Agents

4. Specifying the task environment
Problem specification: Performance measure, Environment, Actuators, Sensors (PEAS).
Example: autonomous taxi
Performance measure : Safe, fast, legal, comfortable trip, maximize profits
Environment : Roads, other traffic, pedestrians, customers
Actuators : Steering wheel, accelerator, brake, signal, horn
Sensors : Cameras, LIDAR, speedometer, GPS, odometer, engine sensors, keyboard

5. Another PEAS example: Spam filter
Performance measure : Minimizing false positives, false negatives
Environment : A user’s account, server
Actuators : Mark as spam, delete, etc.
Sensors : Incoming messages, other information about user’s account

6. Environment types Fully observable / Partially observable
If an agent’s sensors give it access to the complete state of the environment needed to choose an action, the environment is fully observable.

7. Environment types Deterministic / Stochastic
An environment is deterministic if the next state of the environment is completely determined by the current state of the environment and the action of the agent;
In a stochastic environment, there are multiple unpredictable outcomes.
Deterministic
Stochastic

8. Environment types Episodic / Sequential
In an episodic environment, the agent’s experience is divided into atomic episodes. Each episode consists of the agent perceiving and then performing a single action.
In a sequential environment, the agent engages in a series of connected episodes. Current decision can affect future decisions.
Episodic
Sequential

9. Environment types Static / Dynamic
A static environment does not change while the agent is thinking.
The environment is semi-dynamic if the environment itself does not change with the passage of time but the agent's performance score does eg play chess with clock.
Dynamic
Static
Semi-dynamic

10. Environment types Discrete / Continuous
If the number of distinct percepts and actions is limited, the environment is discrete, otherwise it is continuous.
Discrete
Continuous

11. Environment types Single agent / Multi-agent
If the environment contains other intelligent agents, the agent needs to be concerned about, it is a multi-agent.
Single agent
Multi-agent

12. Environment types Known /Unknown
if the rules of the environment (transitions and rewards) known to the agent, it’s known environment. If it’s not known , the agent will have to learn how it works in order to make good decisions.
Unknown
Known

13. Agent Types
Five basic types of Agent
Simple Reflex Agents
Agents do not have memory of past world states or percepts. So, actions depend solely on current percept.
For example if a mars lander found a rock in a specific place it needed to collect then it would collect it, if it was a simple reflex agent then if it found the same rock in a different place , it would still pick it up as it doesn't take into account that it already picked it up.
If tail-light of car in front is red, then brake.

14. Agent Types Five basic types of Agent Model based reflex agents
Agents have internal state, which is used to keep track of past states of the world.
Agents have the ability to represent change in the World.
description of
current world state
Know how world evolves
Overtaking car gets
closer from behind
How agents actions affect the world
Wheel turned clockwise
takes you right

15. Agent Types Five basic types of Agent Goal based agents
Reflex agent breaks when it sees brake lights.
Goal based agent reasons
–Brake light -> car in front is stopping -> I should stop -> I should use brake
Five basic types of Agent
Goal based agents
In addition to state information, have goal information that
describes desirable situations to be achieved.
knowing state and environment
Taxi can go left, right, straight
Have a goal
A destination to get to
Uses knowledge about a goal to guide its actions
E.g., Search, planning

16. Agent Types Five basic types of Agent Utility based agents
When there are multiple possible alternatives, how to
decide which one is best? Many action sequences get taxi
to destination. How fast, how safe…..
A utility function maps a state onto a real number which
describes the associated degree of happiness.

17. Agent Types Five basic types of Agent Learning agents
When we expand our environments we get a larger and larger amount of tasks, eventually we are going to have a very large number of actions to pre-define. Another way of going about creating an agent is to get it to learn new actions as it goes about its business.

18. Agent Types Evaluates current world state Changes action rules
Suggests explorations

19. Learning Agents(Taxi driver)
Performance element
How it currently drives
Taxi driver Makes quick left turn across 3 lanes
Critics observe shocking language by passenger and other drivers and informs bad action
Learning element tries to modify performance elements for future
Problem generator suggests experiment out something called Brakes on different Road conditions
Critics is not always easy
shocking language
Less tip
Less passengers

20. Learning Agents(Taxi driver)
“Quick turn is not safe”
No quick turn
Road conditions
Takes percepts
and selects
actions
Try out the brakes on different road surfaces

21. Solving problems by searching
We will consider the problem of designing goal-based agents in fully observable, deterministic, discrete, known environments.
The agent must find a sequence of actions that reaches the goal.
The performance measure is defined by
reaching the goal and
how “expensive” the path to the goal is

22. Search problem components
Initial state :
Actions :
Transition model :
What state results from performing a given action in a given state?
Goal state :
Path cost :
Assume that it is a sum of nonnegative step costs

23. Example: Romania On vacation in Romania; currently in Arad and
flight leaves tomorrow from Bucharest.

24. Example: Romania Initial state : Arad Actions :
Go from one city to another
Transition model :
If you go from city A to city B, you end up in city B
Goal state : Bucharest
Path cost :
Total distance traveled

25. State space
The initial state, actions, and transition model define the state space of the problem.
State space :
The set of all states reachable from initial state by any sequence of actions.
Can be represented as a directed graph where the nodes are states and links between nodes are actions.

26. Example: Vacuum world Initial State States :
– Agent location and dirt location
– How many possible states?
Actions
– Left, right, suck
Vacuum world state space graph

27. Example: River Crossing
A farmer wants to get his cabbage, sheep, wolf across a river. He has a boat that only holds two. He and at most one item on boat. Unsupervised, wolf bites sheep, sheep eats cabbage. How should computers solve this?

28. Example: River Crossing
State space S : all valid configurations
Initial states = {(CSDF, _ )}  S
Goal states G = {( _ , CSDF )}  S
Cost(s,s’) = 1 for all transitions.
Initial State
Goal State

29. Example: River Crossing
State space S :