Intelligent Systems III Lecture 9: Logical Agents

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Intelligent Systems IIILecture 9 Logical Agents

• Russell and Norvig,
• Artificial Intelligence A Modern Approach
• Chapter 7

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Material

• Slides on wiki
• Chapter 7-10 of AIMA
• Chapter 7 available
• http//aima.cs.berkeley.edu/

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Outline

• Inference rules and theorem proving
• Forward chaining, backward chaining
• Soundness and completeness proofs
• Knowledge-based agents
• Wumpus world
• Model-checking and entailment

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Forward and backward chaining

• Horn Form (restricted)
• KB conjunction of Horn clauses
• Horn clause
• proposition symbol or
• (conjunction of symbols) ? symbol
• E.g., C ? (B ? A) ? (C ? D ? B)
• Modus Ponens (for Horn Form) complete for Horn
  KBs
  
• a1, ,an, a1 ? ? an ? ß
• ß
• Can be used with forward chaining or backward
  chaining.
• These algorithms are very natural and run in
  linear time
  

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Forward chaining

• Idea fire any rule whose premises are satisfied
  in KB,
• add its conclusion to the KB, until query is found

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Forward chaining algorithm

• Forward chaining is sound and complete for Horn KB

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Forward chaining example
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Forward chaining example
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Forward chaining example
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Forward chaining example
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Forward chaining example
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Forward chaining example
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Forward chaining example
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Forward chaining example
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Exercise

• Proof soundness and completeness of forward
  chaining

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Proof of completeness

• FC derives every atomic sentence that is entailed
  by KB
• FC reaches a fixed point where no new atomic
  sentences are derived
  
• Consider the final state as a model m, assigning
  true/false to symbols
  
• Every clause in the original KB is true in m
• a1 ? ? ak ? b
• Hence m is a model of KB
• If KB q, q is true in every model of KB,
  including m
  

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Backward chaining

• Idea work backwards from the query q
• to prove q by BC,
• check if q is known already, or
• prove by BC all premises of some rule concluding
  q
• Avoid loops check if new subgoal is already on
  the goal stack
  
• Avoid repeated work check if new subgoal
• has already been proved true, or
• has already failed

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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Backward chaining example
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Forward vs. backward chaining

• FC is data-driven, automatic, unconscious
  processing,
• e.g., object recognition, routine decisions
• May do lots of work that is irrelevant to the
  goal
• BC is goal-driven, appropriate for
  problem-solving,
• e.g., Where are my keys? How do I get into a PhD
  program?
• Complexity of BC can be much less than linear in
  size of KB

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Knowledge bases

• Knowledge base set of sentences in a formal
  language
  
• Declarative approach to building an agent (or
  other system)
• Tell it what it needs to know
• Then it can Ask itself what to do - answers
  should follow from KB
  
• Agents can be viewed at the knowledge level
• i.e., what they know, regardless of how
  implemented
• Or at the implementation level
• i.e., data structures in KB and algorithms that
  manipulate them

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A simple knowledge-based agent

• The agent must be able to
• Represent states, actions, etc.
• Incorporate new percepts
• Update internal representations of the world
• Deduce hidden properties of the world
• Deduce appropriate actions

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Wumpus World PEAS description

• Performance measure
• gold 1000, death -1000
• -1 per step, -10 for using the arrow
• Environment
• Squares adjacent to wumpus are smelly
• Squares adjacent to 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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Wumpus world characterization

• Fully Observable No only local perception
• Deterministic Yes outcomes exactly specified
• Episodic No sequential at the level of actions
• Static Yes Wumpus and Pits do not move
• Discrete Yes
• Single-agent? Yes Wumpus is essentially a
  natural feature

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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Exploring a wumpus world
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Logic in general

• Logics are formal languages for representing
  information such that conclusions can be drawn
  
• Syntax defines the sentences in the language
• Semantics define the "meaning" of sentences
• i.e., define truth of a sentence in a world
• E.g., the language of arithmetic
• x2 y is a sentence x2y gt is not a
  sentence
  
• x2 y is true iff the number x2 is no less
  than the number y
  
• x2 y is true in a world where x 7, y 1
• x2 y is false in a world where x 0, y 6

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Entailment

• Entailment means that one thing follows from
  another
• KB a
• Knowledge base KB entails sentence a if and only
  if a is true in all worlds where KB is true
• E.g., the KB containing the Dutch won and the
  Belgians won entails Either the Dutch won or
  the Belgians won
  
• E.g., xy 4 entails 4 xy
• Entailment is a relationship between sentences
  (i.e., syntax) that is based on semantics

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Models

• Logicians typically think in terms of models,
  which are formally structured worlds with respect
  to which truth can be evaluated
  
• We say m is a model of a sentence a if a is true
  in m
• M(a) is the set of all models of a
• Then KB a iff M(KB) ? M(a)
• E.g. KB Dutch won and Belgianswon a Dutch won

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Entailment in the wumpus world

• Situation after detecting nothing in 1,1,
  moving right, breeze in 2,1
• Consider possible models for KB assuming only
  pits
• 3 Boolean choices ? 8 possible models

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Wumpus models
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Wumpus models

• KB wumpus-world rules observations

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Wumpus models

• KB wumpus-world rules observations
• a1 "1,2 is safe", KB a1, proved by model
  checking

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Wumpus models

• KB wumpus-world rules observations

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Wumpus models

• KB wumpus-world rules observations
• a2 "2,2 is safe", KB a2

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Wumpus world sentences

• Let Pi,j be true if there is a pit in i, j.
• Let Bi,j be true if there is a breeze in i, j.
• ? P1,1
• ?B1,1
• B2,1
• "Pits cause breezes in adjacent squares"
• B1,1 ? (P1,2 ? P2,1)
• B2,1 ? (P1,1 ? P2,2 ? P3,1)

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Truth tables for inference
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Exercise 7.3 (page 236)

• Suppose the agent has progressed to the point
  shown in Figure 7.4(a) (slide 36), having
  perceived nothing in 1,1, a breeze in 2,1,
  and a stench in 1,2. and is now concerned with
  the contents of 1,3, 2,2, and 3,1. Each of
  these can contain a pit and at most one can
  contain a wumpus.
• Following the example of Figure 7.5, construct
  the set of possible worlds. (You should find 32
  of them.) Mark the worlds in which the KB is true
  and those in which each of the following
  sentences is true
• a2 There is no pit in 2,2.
• a3 There is a wumpus in 1,3.
• Hence show that KB a2 and KB a3.

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Inference by enumeration

• Depth-first enumeration of all models is sound
  and complete
  
• For n symbols, time complexity is O(2n), space
  complexity is O(n)
  

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Proof methods

• Proof methods divide into (roughly) two kinds
• Application of inference rules
• Legitimate (sound) generation of new sentences
  from old
• Proof a sequence of inference rule
  applications Can use inference rules as
  operators in a search algorithm
• Typically require transformation of sentences
  into a normal form
• Model checking
• truth table enumeration (always exponential in n)
• improved backtracking, e.g., Davis--Putnam-Logeman
  n-Loveland (DPLL)
• heuristic search in model space (sound but
  incomplete)
• e.g., min-conflicts-like hill-climbing
  algorithms

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Inference-based agents in the wumpus world

• A wumpus-world agent using propositional logic
• ?P1,1
• ?W1,1
• Bx,y ? (Px,y1 ? Px,y-1 ? Px1,y ? Px-1,y)
• Sx,y ? (Wx,y1 ? Wx,y-1 ? Wx1,y ? Wx-1,y)
• W1,1 ? W1,2 ? ? W4,4
• ?W1,1 ? ?W1,2
• ?W1,1 ? ?W1,3
• ? 64 distinct proposition symbols, 155 sentences

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Expressiveness limitation of propositional logic

• KB contains "physics" sentences for every single
  square
• For every time t and every location x,y,
• Lx,y ? FacingRightt ? Forwardt ? Lx1,y
• Rapid proliferation of clauses

t
t
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Summary

• Resolution is complete for propositional
  logicForward, backward chaining are linear-time,
  complete for Horn clauses
• Logical agents apply inference to a knowledge
  base to derive new information and make decisions
• Wumpus world requires the ability to represent
  partial and negated information, reason by cases,
  etc.
• Propositional logic lacks expressive power

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Exercise

• Show the following using resolution
• KB (B1,1 ? (P1,2? P2,1)) ?? B1,1
• a ?P1,2
• KB a

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Resolution algorithm

• Proof by contradiction, i.e., show KB??a
  unsatisfiable
  

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Conversion to CNF

• B1,1 ? (P1,2 ? P2,1)
• Eliminate ?, replacing a ? ß with (a ? ß)?(ß ?
  a).
• (B1,1 ? (P1,2 ? P2,1)) ? ((P1,2 ? P2,1) ? B1,1)
• 2. Eliminate ?, replacing a ? ß with ?a? ß.
• (?B1,1 ? P1,2 ? P2,1) ? (?(P1,2 ? P2,1) ? B1,1)
• 3. Move ? inwards using de Morgan's rules and
  double-negation
• (?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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Resolution example

• KB (B1,1 ? (P1,2? P2,1)) ?? B1,1
• a ?P1,2