Applications spatiales nécessitant de la planification d’actions concurrente sous incertitude Éric Beaudry 6 juin 2011

2 Robots sur Mars Observation de la Terre

MISSION PLANNING FOR MARS ROVERS Sample application 3 Image Source :

4 Mars Rovers: Autonomy is required Robot Sejourner > 11 Minutes * Light

5 Mars Rovers: Constraints Navigation – Uncertain and rugged terrain. – No geopositioning tool like GPS on Earth. Structured-Light (Pathfinder) / Stereovision (MER). Energy. CPU and Storage. Communication Windows. Sensors Protocols (Preheat, Initialize, Calibration) Cold !

6 Mars Rovers: Uncertainty (Speed) Navigation duration is unpredictable. 5 m 57 s 14 m 05 s

Mars Rovers: Uncertainty (Speed) robot 7

8 Mars Rovers: Uncertainty (Power) Required Power by motors  Energy Level

Mars Rovers: Uncertainty (Size&Time) Lossless compression algorithms have highly variable compression rate. Image size : 1.4 MB Time to Transfer: 12m42s Image size : 0.7 MB Time to Transfer : 06m21s 9

Mars Rovers: Uncertainty (Sun) Sun Normal Vector Normal Vector 10

OBJECTIVES 11

Goals Generating plans with concurrent actions under resources and time uncertainty. Time constraints (deadlines, feasibility windows). Optimize an objective function (i.e. travel distance, expected makespan). Elaborate a probabilistic admissible heuristic based on relaxed planning graph. 12

Assumptions Only amount of resources and action duration are uncertain. All other outcomes are totally deterministic. Fully observable domain. Time and resources uncertainty is continue, not discrete. 13

Dimensions Effects: Determinist vs Non-Determinist. Duration: Unit (instantaneous) vs Determinist vs Discrete Uncertainty vs Probabilistic (continue). Observability : Full vs Partial vs Sensing Actions. Concurrency : Sequential vs Concurrent (Simple Temporal) [] vs Required Concurrency. 14

LITERATURE REVIEW 15

Existing Approaches Planning concurrent actions – F. Bacchus and M. Ady. Planning with Resource and Concurrency : A Forward Chaining Approach. IJCAI MDP : CoMDP, CPTP – Mausam and Daniel S. Weld. Probabilistic Temporal Planning with Uncertain Durations. National Conference on Artificial Intelligence (AAAI) – Mausam and Daniel S. Weld. Concurrent Probabilistic Temporal Planning. International Conference on Automated Planning and Scheduling – Mausam and Daniel S. Weld. Solving concurrent Markov Decision Processes. National Conference on Artificial intelligence (AAAI). AAAI Press / The MIT Press Factored Policy Gradient : FPG – O. Buffet and D. Aberdeen. The Factored Policy Gradient Planner. Artificial Intelligence 173(5-6):722– Incremental methods with plan simulation (sampling) : Tempastic – H. Younes, D. Musliner, and R. Simmons. « A framework for planning in continuous-time stochastic domains. International Conference on Automated Planning and Scheduling (ICAPS) – H. Younes and R. Simmons. Policy generation for continuous-time stochastic domains with concurrency. International Conference on Automated Planning and Scheduling (ICAPS) – R. Dearden, N. Meuleau, S. Ramakrishnan, D. Smith, and R. Washington. Incremental contingency planning. ICAPS Workshop on Planning under Uncertainty

Fully Non-Deterministic (Outcome + Duration) + Action Concurrency FPG [Buffet] + Discrete Action Duration Uncertainty CPTP [Mausam] + Deterministic Outcomes [Beaudry] [Younes] Families of Planning Problems with Actions Concurrency and Uncertainty + Deterministic Action Duration = Temporal Track at ICAPS/IPC Forward Chaining [Bacchus] + PDDL Longest Action CoMDP [Mausam] + Sequential (no action concurrency) [Dearden] MDP Classical Planning A* + limited PDDL The + sign indicates constraints on domain problems.

18 Application 2 : observation de la Terre Conditions d’acquisition (ex: météo) incertaines (très problématique pour les données optiques). Des requêtes urgentes peuvent survenir. Les fenêtres de communications sont limitées. Capacité de stockage limitée sur les satellites. Les changements d’orbite sont coûteux. Volume de données incertain. Besoin de planifier les actions pour optimiser les acquisition de données. Réf.: [Capderou 2002]. RadarSat II

PLANIFICATION CLASSIQUE

Planification classique

Planification temporelle

Planification avec actions concurrentes

MDP : Séquence d’actions avec incertitude

Incertitude sur le temps

COMMENT COMBINER INCERTITUDE, INCERTITUDE SUR LE TEMPS, ET ACTIONS CONCURRENTE ?

Voir diapos 21 à 39 de ma

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