1. Logical Agents

2. Where are we at? Finished up Search Techniques
Exam next Monday
HW3 (MiniMax) assigned (due NEXT Friday)
Today we begin a different style of agent

3. Let’s play a game of Wumpus

4. A Typical Wumpus World

5. Wumpus World Description

6. Let’s Play Wumpus! You are in Cell (1,1) facing “North”
None of the sensors register anything.
Find the gold….

7. Exploring the Wumpus World

8. Exploring the Wumpus World

9. Exploring the Wumpus World

10. Exploring the Wumpus World

11. Exploring the Wumpus World

12. Exploring the Wumpus World

13. Exploring the Wumpus World

14. Exploring the Wumpus World

15. Motivating KB Agents
Reflex agents keep the world clean, in large part, by dumb luck
Searching algorithm can find the shortest path from Arad to Bucharest, but can’t easily adjust when the road from Rimnicu Vilcea to Pitesti is closed for maintenance.
Logic (Knowledge-Based) agents combine general knowledge with current percepts to infer hidden aspects of current state prior to selecting actions

16. Knowledge Bases

17. Knowledge Base A generic KB agent (Fig 7.1)
A knowledge base (KB) is a set of representations (sentences) of facts about the world.
TELL and ASK - two basic operations
to add new knowledge to the KB
to query what is known to the KB
Infer - what should follow after the KB has been TELLed.
A generic KB agent (Fig 7.1)

18. Generic KB-Based Agent

19. Simple Knowledge-Based Agent

20. Representing Knowledge Problems: PAGE Formulation
Percepts
What can agent observe?
What can sensors tell it?
Actions
What actuators does agent have?
In what context are they applicable?
Goals
What are agents goals? Preferences (utilities)?
How does agent evaluate them (check environment, deliberate, etc.)?
Environment
What are “rules of the world”?
How can these be represented, simulated?

21. Wumpus World PAGE Description

22. Wumpus World: PEAS Description

23. Tight spots
(0.86 vs. 0.31)

24. Tight spots
(0.86 vs. 0.31)

25. Tight spots
(0.86 vs. 0.31)

26. Tight spots
(0.86 vs. 0.31)

27. Logic
When we have too many states, we want a convenient way of dealing with sets of states.
The sentence “It’s sunny” stands for all the states of the world in which it is sunny.
Logic provides a way of manipulating big collections of sets by manipulating short descriptions instead.
Instead of thinking about all the ways a world could be, we’re going to work in a language of expressions that describe those sets.

28. What is a logic? A formal language
Syntax – what expressions are legal
Semantics – what legal expressions mean
Proof system – a way of manipulating syntactic expressions to get other syntactic expressions (which will tell us something new)
Why proofs? Two kinds of inferences an agent might want to make:
Multiple percepts  conclusions about the world
Current state & operator  properties of next state

29. Logic in general