Effective Approaches for Partial Satisfaction (Over- subscription) Planning Romeo Sanchez * Menkes van den Briel ** Subbarao Kambhampati * * Department of Computer Science and Engineering ** Department of Industrial Engineering Arizona State University Tempe, Arizona
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Effective Approaches for Partial Satisfaction (Over-subscription) Planning Romeo Sanchez * Menkes van den Briel ** Subbarao Kambhampati * * Department.
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Effective Approaches for Partial Satisfaction (Over-subscription) Planning
Romeo Sanchez *Menkes van den Briel **Subbarao Kambhampati *
* Department of Computer Science and Engineering** Department of Industrial EngineeringArizona State UniversityTempe, Arizona
Outline
Background Example Approaches
Optiplan Altaltps Sapaps
Planning graph heuristics Results
For all your demands, you could’ve bought me a better flash memory stick at least!
In one day achieve the following 100 goals: RockData at WP 1, high-res pics at WP 2 & 3,
…., SoilData at WP 100
No way I can achieve that many goals in one day
It’s hard but here is the best I can do:
Goal1, Goal5, Goal99
Given: Actions with costs, and goals with utilities, find a plan that has a highest {utility – cost}
Previous Approaches:Highest utility goal firstEstimating the set of most beneficial goals
Background
rao
Mention that David Smith brain washed my students with his Summer School talk..
Background
Complete satisfaction (traditional) planning Goal state G is a list of conjunctions: G = g1 g2 … gn
A plan that achieves n – 1 goal fluents is as good as a plan that achieves 0 goal fluents
Partial satisfaction planning (PSP) Goal state G is a list of fluents: G = {g1, g2 , …, gn} Goal fluents might have utilities, actions might have costs,
therefore achieving a partial plan might be more beneficial than the “null” plan.
Achieving all goal fluents might be impossible… The goal state G may contain logically conflicting fluents
There might not be enough resources to achieve all fluents in G
PSP Net benefit: Given a planning problem P = (F, A, I, G), and for each action
a “cost” ca 0, and for each goal fluent f G a “utility” uf 0, and a positive number k. Is there a finite sequence of actions = (a1, a2, …, an) that starting from I leads to a state S that has net benefit f(SG) uf – a ca k.
PLAN EXISTENCE
PLAN LENGTH
PSP GOAL LENGTH
PSP GOAL
PLAN COST PSP UTILITY
PSP UTILITY COST
PSP NET BENEFIT
Example
Getting from Las Vegas (LV) to San Jose (SJ)
C: action cost
U(G): utility of goal G
G1,G2,G3,G4: goals
P = {travel(LV,DL), travel(DL,SJ), travel(SJ,SF)} achieves G1, G2, G3
Approaches
Optiplan Integer programming based STRIPS planner Solves the PSP problem by encoding it as an integer
program
Altaltps Heuristic regression planner Solves the PSP problem through a goal selection heuristic
Sapaps Heuristic forward state space planner Solves the PSP problem using an anytime A* algorithm
Optiplan
Optiplan planning system: Combines Graphplan (Blum & Furst, 1995) with State
Change Encoding (Vossen et al., 1999) As in the Blackbox planning system, Graphplan reduces
the encoding size generated by Optiplan Computes optimal plans for a given parallel length
Objective: fG Uf (x_addf,n + x_preaddf,n + x_maintainf,n) – lL aA Ca ya,l
Sum of goal utilities – Sum of action cost
Optiplan and partial satisfaction
Objective 0 / Minimize #actions
Constraints Fluent changes
Satisfy initial state Satisfy goal
Fluent implications Action implications
Total satisfaction planning: goal satisfaction is treated as a hard constraint
Objective Maximize net benefit
Goal utility – action cost
Constraints Fluent changes
Satisfy initial state
Fluent implications Actions implications
Partial satisfaction planning: goal satisfaction is treated as a soft constraint
Graphplan based cost propagation
AltAltps
AltAlt planning system Heuristic state-space search planner (Nguyen,
AltAltps planning system Total enumeration on 2n goal subsets is too costly Selects a promising subset of the top-level goals upfront Searches for a plan using a regression state space search
combined with cost-sensitive planning graph heuristics.
AltAltps cost propagation
Using a planning graph structure Propositions in the initial state come for free (they have
zero cost) Other propositions have costs computed as follows:
Propagation procedures Max-propagation
Sum-propagation
0
0
0
0
4
0
0
4
5 5
8
5 5
3
l=0 l=1 l=2
hl(p) = Cost of proposition p at level l
0 if p I
hl(p) = min{hl-1(p), cost(a) + Cl(a)} if l > 0
otherwise
Cl(a) = max{hl-1(q) : q prec(a)}
Cl(a) = q prec(a) hl-1(q)
4 4
AltAltps goal set selection
Main idea Start with the original goal set G and an empty goal set G’ Iteratively add goals to G’ as long as the estimated NET
BENEFIT increases The cost of adding another goal g to G’ depends on the
goals that are already in G’
G’ G’ g
Cost for achieving G’
Residual cost for gRelaxed plan for G’ (R’p)
Rp for G’ g biased to re-use actions in R’p
AltAltps cost-sensitive relaxed plan heuristic
General procedure States are ranked during search using the relaxed plan
heuristic and the propagated costs The idea is to compute the cost of a relaxed plan Rp in
terms of the costs of the actions composing it.
Heuristic value for S equal h(S) = aRpcost(a)
1. Given a state S, remove the (sub)goal g from S that has highest hl(g)
2. Select the action that supports g with lowest cost (cost(a) + Cl(a))
3. Regress S over a to get S’ = S prec(a) \ eff(a)
4. Stop when each proposition q S is present in the initial state
Anytime A* Algorithm:Search through best beneficial nodes
SAPAPS: a forward A* approach for PSP
Heuristic: Variation of SAPA’s ApproachHeuristically extracting the least cost relaxed plan using cost-functionRemove “unbeneficial” goals and related actions