1. Lecture 5-2 February 4th, 1999 CS250
FOPL, Part Deux
Lecture 5-2
February 4th, 1999
CS250
Lecture 5-2
CS250: Intro to AI/Lisp

2. Return to the Land of the Wumpus
Use Tell to add assertions to the knowledge base
Use Ask to pose queries
Answers to Asked queries are binding lists (aka substitutions)
Tell(KB,Percept([Smell, Breeze, None],5)) Ask(KB,a Action(a, 5))
Does the KB entail any particular action at t=5?
{a/Shoot} (That’s a yes)
Lecture 5-2
CS250: Intro to AI/Lisp

3. A Little Syntax Given a sentence S, and a substitution, 
S  denotes the result of plugging  into S
S = Smarter(x, y)
 = {x/Hillary, y/Bill}
S  = Smarter(Hillary, Bill)
What does Ask(KB, S) do in these terms?
Returns some (or all)  such that:
KB |= S
Lecture 5-2
CS250: Intro to AI/Lisp

4. KB for WW Represent perceptions more abstractly
Write rules to transform percepts into predicates
Map from predicates to actions
Why is this a better approach?
Lecture 5-2
CS250: Intro to AI/Lisp

5. “The Only Constant is Change”
How do we reason about changing situations?
Minimal internal state, but story history of percepts
Build an internal model of the world
Need diachronic rules
Already use time, but not the whole context
Lecture 5-2
CS250: Intro to AI/Lisp

6. Be here now. Predicate calculus doesn’t have time for time
“For all x” means “for all x, today, tomorrow and the day after, and the day after that...”
Fine for the immutable things in this world, but need a fix for changing predicates
Lecture 5-2
CS250: Intro to AI/Lisp

7. Three’s Company Add situations to predicate calculus Lecture 5-2
CS250: Intro to AI/Lisp

8. Ever-Changing Predicates
Add a situation argument to properties that change over time, Si
Actions move from one situation to the next situation
Result(action, situation)
Actions have effects
Lecture 5-2
CS250: Intro to AI/Lisp

9. Tracking Action Effects
If the agent is at a location with gold, and grabs the gold, the agent is then holding the gold in the next situation
Portable(Gold)
s AtGold(s)  Present(Gold, s)
x,s Present(x,s)  Portable(x)  Holding(x, Result(Grab, s))
And if the agent releases the gold?
x,s Holding(x, Result(Release, s))
Lecture 5-2
CS250: Intro to AI/Lisp

10. Flavors of Axioms State the effects of actions (Effect axioms)
What about the non-effects of inaction? (Frame axioms)
If the agent is Holding an object in a situation, and performs any action except Release, then the agent is Holding the object in the resulting situation
Don’t forget about not Holding!
Lecture 5-2
CS250: Intro to AI/Lisp

11. A New Flavor Combination
Successor state axioms combine effect and frame axioms
Each axiom is “about” a predicate:
true afterwards  [an action made it true
 true already and no action made it false]
Lecture 5-2
CS250: Intro to AI/Lisp

12. Frame Problems Representational frame problem
Writing down what doesn’t change
Inferential frame problem
Reasoning about what doesn’t change
Situation calculus requires carrying every property through time - even if it doesn’t change
What if most of the world doesn’t change?
Lecture 5-2
CS250: Intro to AI/Lisp

13. Doin’ the Sherlock Thing
Squares are near a breezy pit
Diagnostic - infer cause from effect
Causal - infer effect from cause
Model-based reasoning versus diagnostic approaches
Lecture 5-2
CS250: Intro to AI/Lisp

14. Planning Special search systems for reasoning about actions
Different representations
Making good choices
Preferences among actions
Lecture 5-2
CS250: Intro to AI/Lisp