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74.419 Artificial Intelligence 2005/06
Partial Order Planning
Socks & Shoes
Left Sock
Start
Finish
Right Shoe
Left Shoe
Right Sock
Start
Right Sock
Finish
LeftShoe
RightShoe
Left Sock
Start Start Start Start Start
Right Sock
Right Sock
Right Sock
Right Sock
Right Sock
Left Sock
Left Sock
Left Sock
Left Sock
Left Sock
Left Sock
RightShoe
RightShoe
RightShoe
RightShoe
RightShoe
LeftShoe
LeftShoe
LeftShoe
LeftShoe
Finish Finish Finish Finish Finish
Left Shoe on
Right Shoe on
Left Sock on
Right Sock on
Partial Order Plan:
Total Order Plans:
Partially Ordered Plans
Partially Ordered Plans - or:
"How Do You Put Your Shoes On?"
Partially Ordered Plans:
• no strict sequence
• partly parallel
• observe threats
Resource Constraints in Planning
Resources physical quantities, e.g. money, fluids etc. time
Integrate Measures into Action Description and Planning representation of physical quantities and
reasoning / calculation, e.g. for buy-action: effect: cash := cash – price (x)
time system / time logic, e.g. go-to-action: effect: time := time + 30 (Minutes)
Backtracking on Constraint Violation
Least Commitment Strategy Partially Instantiated Plans
Least Commitment Strategy
In general, make as little concrete as possible, i.e. leave things undetermined until you have to determine them and become concrete.
Partially Instantiated Plans
During planning, variables have not necessarily to be instantiated immediately.
Instantiation can wait, until binding becomes necessary
Partial Order Planning 1
Start with a rough plan and refine iteratively.
First plan consists only of start and finish actions: start - T as precondition, initial world state as effect finish - goal as precondition, NIL as effect
Select actions to achieve sub-goals separately, quasi in parallel partial-order plan
Fulfill open preconditions (sub-goals), until no more unsatisfied preconditions are left (last one is T of start)
Partial Order Planning - Causal Links
Add causal links to connect effects from actions to matching preconditions for plan, e.g. move(A,B,x) has effect clear(B) clear(B) is precondition for move(B,y,z)
Causal links specify a partial order.
effect of move (A,y,B) is on(A,B) is precondition for finish (goal state)
causal link
Partial Order Planning - Threats
Recognize threats - the effect of an action A destroys the precondition of another action B, e.g. move(A,x,B) destroys clear(B) (in DELETE-list) clear(B) is precondition for move(B,y,z) thus, move(B,Fl,C) has to be done before move
(A,Fl,B)
Add threats as partial order to plan: b<a (do b before a).
effect of a = move(A,Fl,B) includes DEL Clear(B)
precond of c = move(B,Fl,C) includes Clear(B)
threat!c<a ca
b
threatb<c
Partial Order Planning - Threats
partial order plan = set of action strings (partial plans)
Problem:
Detect and resolve threats, i.e. conflicts between actions – where the precondition of one action is deleted by another action – by choosing an adequate ordering of actions: if action b is a threat to action a, then a<b, i.e. a has to occur before b.
(see also Russell/Norvig textbook, The POP Planner)
Partial Order Planning - Overall
Use plan transformation operators to refine the partial plan and construct a complete plan: add an action (operator), reorder actions (operators), instantiate actions (operators).
A partial order plan consists of a set of action sequences (partial plans; action strings) which together achieve the complete set of goal literals.
Threats induce an additional partial order of these action sequences.
Additional References
Nils J. Nilsson: Artificial Intelligence – A New Synthesis. Morgan Kaufmann, San Francisco, 1998.
Konolidge, K. and K. Myers: The Saphira Architecture for Autonomous Mobile Robots (Robot Soccer Class Project)
Guzzoni, D. et al.: Many Robots Make Short Work. (AAAI’96 Robot Competition - Meeting Scheduling)
Martina Veloso, MIT (RoboCup)