3. Logical Agents
• Humans can know “things” and “reason”
– Representation: How are the things stored?
– Reasoning: How is the knowledge used?
• To solve a problem…
• To generate more knowledge…
• Knowledge and reasoning are important to artificial agents
because they enable successful behaviors difficult to achieve
otherwise
– Useful in partially observable environments
• Can benefit from knowledge in very general forms,
combining and recombining information
February 20, 2006 AI: Chapter 7: Logical Agents 3
4. Knowledge-Based Agents
• Central component of a Knowledge-Based Agent is
a Knowledge-Base
– A set of sentences in a formal language
• Sentences are expressed using a knowledge representation
language
• Two generic functions:
– TELL - add new sentences (facts) to the KB
• “Tell it what it needs to know”
– ASK - query what is known from the KB
• “Ask what to do next”
February 20, 2006 AI: Chapter 7: Logical Agents 4
5. Knowledge-Based Agents
• The agent must be able to:
Domain-
– Represent states and actions Independent
Algorithms
– Incorporate new percepts Inference Engine
– Update internal Knowledge-Base
representations of the world
– Deduce hidden properties of
Domain-
the world Specific
Content
– Deduce appropriate actions
February 20, 2006 AI: Chapter 7: Logical Agents 5
7. Knowledge-Based Agents
• Declarative
– You can build a knowledge-based agent simply by
“TELLing” it what it needs to know
• Procedural
– Encode desired behaviors directly as program
code
• Minimizing the role of explicit representation and
reasoning can result in a much more efficient system
February 20, 2006 AI: Chapter 7: Logical Agents 7
8. Wumpus World
• Performance Measure
– Gold +1000, Death – 1000
– Step -1, Use arrow -10
• Environment
– Square adjacent to the Wumpus are smelly
– Squares adjacent to the pit are breezy
– Glitter iff gold is in the same square
– Shooting kills Wumpus if you are facing it
– Shooting uses up the only arrow
– Grabbing picks up the gold if in the same square
– Releasing drops the gold in the same square
• Actuators
– Left turn, right turn, forward, grab, release,
shoot
• Sensors
– Breeze, glitter, and smell
February 20, 2006 AI: Chapter 7: Logical Agents 8
9. Wumpus World
• Characterization of Wumpus World
– Observable
• partial, only local perception
– Deterministic
• Yes, outcomes are specified
– Episodic
• No, sequential at the level of actions
– Static
• Yes, Wumpus and pits do not move
– Discrete
• Yes
– Single Agent
• Yes
February 20, 2006 AI: Chapter 7: Logical Agents 9
18. LOGIC
Syntax:
– Knowledge base consists of sentences
– These sentences are expressed according to the
syntax.
Ex: “x + y=4” is a well formed sentence.
Semantic:
• Meaning of the sentence.
• Mention the truth of each sentence.
19. Logic
• Entailment means that one thing follows logically
from another
a |= b
• a |= b iff in every model in which a is true, b is also
true
• if a is true, then b must be true
• the truth of b is “contained” in the truth of a
February 20, 2006 AI: Chapter 7: Logical Agents 19
20. Example
• “X+Y=4” is true where X is 2
– And Y is 2
• But false where x is 1 and y is 1.
• (every sentence must be true or false in each
possible world.)
21. Entailment
• Relation of logical element.
• A sentence follows logically from another
sentence.
• Notation : αl= β
– Means sentence a entails(require, need, improve,
demand) β
– If every word of α is true and β is also true.
– (simply if α is true then β must be true)
22. Logic in Wumpus world
• The agent detecting nothing in
[1,1]
• The agent is interested in
whether the adjacent squares
[1,2] & [2,2] and [3,1].
• Each of these squares may or
may not contain a pit.
• So 23 = 8 possible models.
25. Conclusion
• α1 : there is no pit in [1,2]
• α2 : there is no pit in [2,2]
• KB is false in any model in which [1,2]
contains pit ,because there is no breeze in
[1,1]
We know if KB is true α1 is also true.
Hence KB is not equal to α1
in some models, if KB is true α2 is false
KB V α2 so the agent cannot conclude that
there is no pit in [2,2]
26. Inference (assumption or conclusion)
• The inference algorithm I can be derive from
KB, then
•KB |-i a
• a is derived from KB by i” (or)
• “ i derives a from KB”
27. Sound
• An inference algorithm derives only entailed sentences is called
“Sound or Truth Preserving”.
• Soundness is highly desirable property.
Correspondence between world and
representation
sentences entails sentence
semantics
semantics
Representation
World
Aspects of the Aspects of the
real world real world
28. Illustration
Sentences are physical configuration of the
agent.
Reasoning is the process of constructing new
physical configurations from old ones.
If any connection between logical reasoning
process and the real environment in which
the agent exists is called “grounding”
