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. 50

3. Knowledge-Based Agent
function KB-AGENT(percept) returns an action static: KB, counter t=0
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. 196

4. 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)
From Figure 7.7, p. 205

5. 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, EXTEND(P,true,model)) and TT-CHECK-ALL(KB, , rest, EXTEND(P,false,model))
From Figure 7.4, p. 209