probabilistic reasoning for robust plan execution steve schaffer, brad clement, steve chien...
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![Page 1: Probabilistic Reasoning for Robust Plan Execution Steve Schaffer, Brad Clement, Steve Chien {first_name.last_name}@jpl.nasa.gov Artificial Intelligence](https://reader033.vdocuments.net/reader033/viewer/2022051517/56649ed25503460f94be0eda/html5/thumbnails/1.jpg)
Probabilistic Reasoningfor Robust Plan Execution
Steve Schaffer, Brad Clement, Steve Chien{first_name.last_name}@jpl.nasa.gov
Artificial Intelligence GroupJet Propulsion Laboratory
California Institute of Technology
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Main Idea
• Represent uncertainty of action effects and durations as parametric, continuous probability distribution functions
• Propagate distributions through plan to project states/resources
• Score plan based on risk– risk = probability outside limits
• Plan to reduce risk
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“Full Probabilistic” Modeling
Durations and resource usage normally distributed
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Modeling Approximations
• Full probabilistic
• Means only– Track only the expected value (mean)– Same as non-probabilistic risk-ignorant
• Pessimistic– Track only “worst” possible value– “Worst” depends on domain / resource
• Single peak– For time-dependent multimodals– Track only one “average” Gaussian
• Chebyshev bound– Distribution-free limit on probability density– Only track the mean and standard deviation
single peak
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Evaluation Domains
• Abstract testbed– one resource– various consumers, replenishers– schedule within time horizon– conflicts resolvable
• Orbiter domain– image planet, process, downlink– ~10 resources, ~10 actions– schedule goals within horizon– conflicts not all resolvable – must minimize
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Evaluation Methodology
• Generate plan (batch mode)– Use different approximations– Planner is not allowed to remove goals
• Run plan on stochastic simulator
• Score execution by # errors caused– error = resource oversubscribed
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Planning in ASPEN
Start (if conflicts exist and user time-limit not exceeded)
...Select probable conflict
Select a repair method ...move
...
...
Select an activity
Select a start time
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Results: Abstract Domain
– Full probabilistic performs best
– Single peak performs well when variance low
– Chebyshev worst
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Results: Runtime
Full probabilistic
Means only
Pessimistic Single peak
Chebyshev
consumable 35s 2s 2s 30s 25s
consumable 2x std dev
35s 2s 2s 35s 25s
non-consumable
3s 3s 400s 5s 600s
Mean run times on the abstract domain problems
Runs were terminated after 2000 iterations
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Results: Orbiter Domain
– Full probabilistic performs best
– Single peak performs well
– Chebyshev worst
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Results: Problem Size
– Means only worse on average for larger problems
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Results: Problem Difficulty
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Results: User Risk Metric
– Means only worse on average for low risk tolerance
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Conclusions
• Alternative methods for handling uncertain continuous variables
• Full probabilistic reasoning is most robust– superior plans
• fewer errors• tailored to user risk attitudes
– but requires modeling overhead– and computationally expensive– suited for high risk-averseness / cost of failure
• Naive approximations do almost as well
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Future Directions
• Direct temporal constraints
• Domain-specific pessimistic approximation
• Need to also evaluate– bounded distributions– particle filter representation
• Integration with execution system– observations update distributions– dynamic replanning
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Overcoming Normal Representation Inaccuracies
• Normals give probability to valuesfrom -∞ to ∞
• Variable domain inaccuracy– duration must be greater than zero– usage is either > 0 or < 0 (if replenishing)– more problematic with small means and high variance
• Timeline domain inaccuracy– Resources often have only one bound of conflict
(e.g. can’t have an overfull battery)– Becomes a problem for mixture of consumers and replenishers
μ0
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• Solution to variable domain inaccuracy– redistribute impossible value probability into
normal
• Timeline domain inaccuracy– move impossible value probability
into a spike withsame integral
Overcoming Normal Representation Inaccuracies
μ0
μ0
μ0
μ0
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Particle Filter Representation
• Commonly used for robot localization
• A Monte Carlo simulation draws sample values (particles) from source random variables toderive likelihoods of alternative states
• In a planner, the particles approximate state/resource projections; the more particles, the more precise the estimate
• Gets around exponential peak computations of normal representation by trading precision and time
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Handling Temporal Constraints
• A good execution system can issue a command when a preceding activity finishes.
• When activities are giventemporal constraints (e.g. back-to-back), thereshould be no probability ofoverlap.
• To handle this, a Bayes net can be constructed based on temporal constraints to calculate the resource distribution resulting from different possible usage contribution combinations.
Prob.
Non.