1. Supplemental slides for CSE 327 Prof. Jeff Heflin
Ch. 7 – Logical Agents
Supplemental slides for CSE 327
Prof. Jeff Heflin

2. Goal-Based Agent Environment Agent sensors State
What the world is like now
How the world evolves
Environment
What it will be like if I do action A
What my actions do
What action I should do now
Goals
actuators
Agent
From Fig. 2.13, p. 52

3. Knowledge-Based Agent
function KB-Agent(percept) returns an action persistent: KB, a knowledge base t, a counter, initially 0 indicating time
Tell(KB, Make-Percept-Sentence(percept, t)) action  ASK(KB, Make-Action-Query(t)) Tell(KB, Make-Action-Sentence(action, t)) t  t + 1 return action
From Figure 7.1, p. 236

4. Example KB Sentences For an adventure game AI, specified informally:
State of the world
I am healthy
A wall is in front of me
Effects of actions
If I fall in a pit, I will die
If I shoot something, I will injure it
Changing goals
If I see an enemy, then attack
If I am injured, then retreat

5. Grammar for Propositional Logic
Sentence  AtomicSentence | ComplexSentence
AtomicSentence  True | False | Symbol
Symbol  P | Q | R | …
ComplexSentence  ( Sentence ) | [ Sentence ] |  Sentence | (Sentence  Sentence) | (Sentence  Sentence) | (Sentence  Sentence) | (Sentence  Sentence)
From Figure 7.7, p. 244

6. Checking Entailment Entailed! Not Entailed! Entailed!
Assume KB={PQ, Q, QR} P Q R
1:
PQ
2: Q
3:
QR
KB:
1  2  3 P
PR F T

7. Inference via Model Checking
function TT-Entails?(KB, ) returns true or false symbols  a list of the proposition symbols in KB and  return TT-Check-All(KB, , symbols, {})
function TT-Check-All(KB, , symbols, model) returns true or false
if Empty?(symbols) then if PL-True?(KB, model) then return PL-True?(, model) else return true else do P First(symbols); rest  Rest(symbols)
return TT-Check-All(KB, , rest, model  {P=true} and TT-Check-All(KB, , rest, model  {P=false})
From Figure 7.10, p. 248
This topic will not be covered this year.

8. Wumpus World Agent
function HYBRID-WUMPUS-AGENT(percept) returns an action inputs: percept, a list [stench, breeze, glitter] persistent: KB, a knowledge base, contains “rules” of the Wumpus world x, y, orientation, the agent’s position visited, array of squares visited by agent, initially empty action, most recent action, initially null plan, an action sequence, initially empty
update x, y, orientation, visited based on action if stench then Tell(KB, Sx,y) else Tell(KB, Sx,y)
if breeze then Tell(KB, Bx,y) else Tell(KB, Bx,y)
if glitter then action  grab else if plan is nonempty then action  Pop(plan) else if for some frontier square [i,j], Ask(KB, (Pi,j  Wi,j )) is true or
for some frontier square [i,j], Ask(KB, (Pi,j  Wi,j )) is false then do
plan  A*-Graph-Search(Route-Problem([x,y], orientation, [i,j], visited)) action  Pop(plan) else action  a randomly chosen move return action
Simplified version of agent described in Figure 7.20, p. 270