1. Some Additional Notes about GOAP
H. Muñoz-Avila

2. FSM vs Planning FSM: Planning Operators A resulting plan:
Patrol
Preconditions:
No Monster
Effects:
patrolled
Fight
Monster in sight
Patrol
Fight
Monster In Sight
No Monster
FSM:
A resulting plan:
Patrol
patrolled
Fight
No Monster
Monster in sight
Neither is more powerful than the other one

3. But Planning Gives More Flexibility
“Separates implementation from data” --- Orkin
inference
knowledge
Many potential plans:
Patrol
Fight …
Planning Operators
Patrol
Preconditions:
No Monster
Effects:
patrolled
Fight
Monster in sight …
If conditions in the state change making the current plan unfeasible: replan!

4. Issues: Efficiency (I)
Evaluating preconditions:
Current state represented in a table for rapid access
Monster in sight
Treasure in sight …. …. √ ×
(fast AI planners like TALPlan also answers with constant complexity the question if a given atom is satisfied in the current state)

5. Issues: Efficiency (II)
Search:
They use A* to guide the search
Operators are prioritize according to domain knowledge ….
(fast AI planners like FastForward also use heuristics to guide search)

6. But What About the Sussman Anomaly?
Principle: Given some goals, achieving one of them will make it impossible to achieve the other ones A B C
Initial State:
Goal:
Classical example in Blocks world:
Any domain-specific strategy in Blocks world that will avoid the problem?

7. But… Does it Work in Practice?
FEAR not!