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Logical Agents
Chapter 7
Spring 2007
Copyright, 1996 © Dale Carnegie & Associates, Inc.
A knowledge-based agent
Accepting new tasks in explicit goals
Knowing about its world
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current state of the world, unseen properties from
percepts, how the world evolves
help deal with partially observable environments
help understand “John threw the brick thru the
window and broke it.” – natural language
understanding
Reasoning about its possible course of actions
Achieving competency quickly by being told or
learning new knowledge
Adapting to changes by updating the relevant
knowledge
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Knowledge Base
A knowledge base (KB) is a set of representations
(sentences) of facts about the world.
TELL and ASK - two basic operations
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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.
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A generic KB agent (Fig 7.1)
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Three levels of A KB Agent
Knowledge level (the most abstract)
Logical level (knowledge is of sentences)
Implementation level
Building a knowledge base
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A declarative approach - telling a KB agent what it
needs to know
A procedural approach – encoding desired behaviors
directly as program code
A learning approach - making it autonomous
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Specifying the environment
The Wumpus world (Fig 7.2) in PEAS
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Performance: +1000 for getting the gold, -1000 for being dead, -1
for each action taken, -10 for using up the arrow
 Goal: bring back gold as quickly as possible
Environment: 4X4, start at (1,1) ...
Actions: Turn, Grab, Shoot, Climb, Die
Sensors: (Stench, Breeze, Glitter, Bump, Scream)
It’s possible that the gold is in a pit or surrounded by pits -> try
not to risk life, just go home empty-handed
The variants of the Wumpus world – they can be
very difficult
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Multiple agents
Mobile wumpus
Multiple wumpuses
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Wumpus World PEAS description
Performance measure
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gold +1000, death -1000
-1 per step, -10 for using the arrow
Environment
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Squares adjacent to wumpus are smelly
Squares adjacent to a 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 gold if in same square
Releasing drops the gold in same square
Sensors: Stench, Breeze, Glitter, Bump, Scream
Actuators: Left turn, Right turn, Forward, Grab, Release, Shoot
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Acting & reasoning
Let’s play the wumpus game!
The conclusion: “what a fun game!”
Another conclusion: If the available
information is correct, the conclusion is
guaranteed to be correct.
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Logic
The primary vehicle for representing
knowledge
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Simple
Concise
Precise
Can be manipulated following rules
It cannot represent uncertain knowledge
well (so it’s where new research is about)
We will learn Logic first and other
techniques later
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Logics
A logic consists of the following:
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A formal system for describing states of
affairs, consisting of syntax (how to make
sentences) and semantics (to relate
sentences to states of affairs).
A proof theory - a set of rules for deducing
the entailments of a set of sentences.
Some examples of logics ...
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Propositional Logic
In this logic, symbols represent whole
propositions (facts)
e.g., D means “the wumpus is dead”
W1,1 Wumpus is in square (1,1)
S1,1 there is stench in square (1,1).
Propositional logic can be connected using
Boolean connectives to generate sentences
with more complex meanings, but does not
specify how objects are represented.
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Other logics
First order logic represents worlds using
objects and predicates on objects with
connectives and quantifiers.
Temporal logic assumes that the world
is ordered by a set of time points or
intervals and includes mechanisms for
reasoning about time.
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Other logics (2)
Probability theory allows the
specification of any degree of belief.
Fuzzy logic allows degrees of belief in a
sentence and degrees of truth.
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Propositional logic
Syntax
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A set of rules to construct sentences:
 and, or, imply, equivalent, not
 literals, atomic or complex sentences
 BNF grammar (Fig 7.7, P205)
Semantics
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Specifies how to compute the truth value of
any sentence
Truth table for 5 logical connectives (Fig 7.8)
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Knowledge Representation
Knowledge representation
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Syntax - the possible configurations that can
constitute sentences
Semantics - the meaning of the sentences
 x > y is a sentence about numbers; or x+y=4;
 A sentence can be true or false
 Defines the truth of each sentence w.r.t. each possible world
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What are possible worlds for x+y = 4
Entailment: one sentence logically follows
another
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 |= , iff  is true,  is also true
Sentences entails sentence w.r.t. aspects follows
aspect (Fig 7.6)
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Reasoning
KB entails sentence  if KB is true,  is true
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Model checking (Fig 7.5) for two sentences/models
 Asking whether KB entails s given KB?
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1 = “There is no pit in [1,2]” -> yes or no?
2 = “There is no pit in [2,2]” -> yes or no?
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An inference procedure
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can generate new valid sentences or verify if a sentence is
valid given KB
is sound if it generates only entailed sentences
A proof is the record of operation of a sound
inference procedure
An inference procedure is complete if it can find a
proof for any sentence that is entailed.
Sound reasoning is called logical inference or
deduction.
A reasoning system should be able to draw
conclusions that follow from the premises, regardless
of the world to which the sentences are intended to
refer.
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Equivalence, validity, and
satisfiability
Logical equivalence requires  |= and  |= 
Validity: a sentence  is true in all models
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Valid sentences are tautologies (P v !P)
Use validity to prove “deduction theorem”: for any 
and ,  |= iff the sentence ( ) is valid
Satisfiability: a sentence  is satisfiable if it is
true in some models
E.g., A v B, P
  |= iff the sentence ( ^ !) is unsatisfiable or !(
^ !) is valid .
Validity and satisfiability:  is valid iff ! is
unstatisfiable; contrapositively,  is satisfiable iff ! is
not valid.
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Inference
Truth tables can be used not only to define the
connectives, but also to test for validity:
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If a sentence is true in every row, it is valid.
 What is a truth table for “Premises imply Conclusion”
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A simple knowledge base for Wumpus
 A simple KB with five rules (P208)
 What if we write R2 as B1,1 => (P1,2 v P2,1)
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Think about the definition of =>
KB |= . Let’s check its validity (Fig 7.9)
 E.g., in Figure 7.9, there are three true models for the KB with
5 rules.
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A truth-table enumeration algorithm (Fig 7.10)
 There are only finitely many models to examine, but it is
exponential in size of the input (n)
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Can we prove this?
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Reasoning Patterns in Prop Logic
 |= iff the sentence ( ^ !) is unstatisfiable
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 are known axioms, thus true (T)
Proof by refutation (or contradiction): assuming  is F, !  is T,
we now need to prove !(^T) is valid, …
Inference rules
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Modus Ponens, AND-elimination, Bicond-elimination
All the logical equivalences in Fig 7.11
A proof is a sequence of applications of inference rules
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An example to conclude neither [1,2] nor [2,1] contains a pit
 Start with R2
Monotonicity (consistency): the set of entailed sentences
can only increase as information is added to KB
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For  and , if KB |=  then KB^ |= 
Propositional logic and first-order logic are monotonic
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Resolution – an inference rule
An example of resolution
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R11, R12 (new facts added), R13, R14 (derived from
R11, and R12), R15 from R3 and R5, R16, R17 –
P3,1 (there is a pit in [3,1]) (P213)
Unit resolution: l1 v l2 …v lk, m = !li
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We have seen examples earlier
Full resolution: l1 v l2 …v lk, m1 v…v mn where li
= mj
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An example: (P1,1vP3,1, !P1,1v!P2,2)/P3,1v!P2,2
Soundness of resolution
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Considering literal li,
 If it’s true, mj is false, then …
 If it’s false, …
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Refutation completeness
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Resolution can always be used to either
confirm or refute a sentence
Conjunctive normal form (CNF)
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A conjunction of disjunctions of literals
A sentence in k-CNF has exactly k literals per
clause (l1,1 v … v l1,k) ^…^ (ln,1 v …v ln,k)
A simple conversion procedure (turn R2 to
CNF, next slide or see P.215)
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Conversion to CNF
B1,1  (P1,2  P2,1)
1. Eliminate , replacing α  β with (α  β)(β  α).
(B1,1  (P1,2  P2,1))  ((P1,2  P2,1)  B1,1)
2. Eliminate , replacing α  β with α β.
(B1,1  P1,2  P2,1)  ((P1,2  P2,1)  B1,1)
3. Move  inwards using de Morgan's rules and doublenegation:
(B1,1  P1,2  P2,1)  ((P1,2  P2,1)  B1,1)
4. Apply distributivity law ( over ) and flatten:
(B1,1  P1,2  P2,1)  (P1,2  B1,1)  (P2,1  B1,1)
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A resolution algorithm (Fig 7.12)
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An example (KB= R2^R4, to prove !P1,2, Fig. 7.13)
Completeness of resolution
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Ground resolution theorem
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Horn cluases
A Horn clause is a disjunction of literals of which
at most one is positive
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An example: (!L1,1 v !Breeze V B1,1)
An Horn sentence can be written in the form
P1^P2^…^Pn=>Q, where Pi and Q are nonnegated
atoms
Deciding entailment with Horn clauses can be done in
linear time in size of KB
Inference with Horn clauses can be done thru forward
and backward chaining
 Forward chaining is data driven
 Backward chaining works backwards from the query, goal-
directed reasoning
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An Agent for Wumpus
The knowledge base (an example on p208)
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Bx,y  …, Sx,y …
There is exactly one W: (1) there is at least one
W, and (2) there is at most one W
Finding pits and wumpus using logical inference
Keeping track of location and orientation
Translating knowledge into action
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A1,1^EastA^W2,1=>!Forward
Problems with the propositional agent
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too many propositions to handle (“Don’t go forward if…”)
hard to deal with change (time dependent propositions)
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Summary
Knowledge is important for intelligent agents
Sentences, knowledge base
Propositional logic and other logics
Inference: sound, complete; valid sentences
Propositional logic is impractical for even very
small worlds
Therefore, we need to continue our AI class
...
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