Lecturer: Austin Tate Date Prepared: 6-Nov-2009 1 Overview Deep - - PDF document

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AI Planner Applications

Practical Applications of AI Planners

Lecturer: Austin Tate Date Prepared: 6-Nov-2009

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AI Planner Applications 2

Overview

Deep Space 1 Other Practical Applications of AI Planners Common Themes

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AI Planner Applications 3

Literature

• Deep Space 1 Papers
• Ghallab, M., Nau, D. and Traverso, P., Automated Planning – Theory and

Practice, chapter 19,. Elsevier/Morgan Kaufmann, 2004.

• Bernard, D.E., Dorais, G.A., Fry, C., Gamble Jr., E.B., Kanfesky, B., Kurien, J.,

Millar, W., Muscettola, N., Nayak, P.P., Pell, B., Rajan, K., Rouquette, N., Smith, B., and Williams, B.C. Design of the Remote Agent experiment for spacecraft autonomy. Procs. of the IEEEAerospace Conf., Snowmass, CO, 1998.

• http://nmp.jpl.nasa.gov/ds1/papers.html
• Other Practical Planners
• Ghallab, M., Nau, D. and Traverso, P., Automated Planning – Theory and

Practice, chapter 22 and 23. Elsevier/Morgan Kaufmann, 2004

• Tate, A. and Dalton, J. (2003) O-Plan: a Common Lisp Planning Web Service,

invited paper, in Proceedings of the International Lisp Conference 2003, October 12-25, 2003, New York, NY, USA, October 12-15, 2003.

• http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003-luc-tate-oplan-web.doc

Literature

Deep Space 1 is described in Chapter 19 in the course textbook: Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice. Elsevier/Morgan Kaufmann, 2004. Further practical planning systems are described in several chapters

• f the course textbook… e.g. chapters 22 and 23.

Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice. Elsevier/Morgan Kaufmann, 2004.

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NASA’s Deep Space 1 (DS1)

http://nmp.jpl.nasa.gov/ds1/ Deep Space 1 launched from Cape Canaveral on October 24, 1998. During a highly successful primary mission, it tested 12 advanced, high-risk technologies in

• space. In an extremely successful extended mission, it

encountered Comet Borrelly and returned the best images and other science data ever from a comet. During its fully successful hyperextended mission, it conducted further technology tests. The spacecraft was retired on December 18, 2001. IPS = Ion Propulsion System (one of the advanced technologies being demonstrated on DS1)

AI Planner Applications 4

Deep Space 1 – 1998-2001

http://nmp.jpl.nasa.gov/ds1/

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NASA’s Deep Space 1 (DS1) at Comet Borrelly

http://nmp.jpl.nasa.gov/ds1/

AI Planner Applications 5

DS 1 – Comet Borrelly

http://nmp.jpl.nasa.gov/ds1/

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf

AI Planner Applications 6

Achieve diverse goals on real spacecraft

High Reliability

• single point failures
• multiple sequential failures

Tight resource constraints

• resource contention
• conflicting goals

Hard-time deadlines Limited Observability Concurrent Activity

DS1 Domain Requirements

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf

AI Planner Applications 7

Constraint-based planning and scheduling

• supports goal achievement, resource constraints,

deadlines, concurrency

Robust multi-threaded execution

• supports reliability, concurrency, deadlines

Model-based fault diagnosis and

reconfiguration

• supports limited observability, reliability, concurrency

Real-time control and monitoring

DS1 Remote Agent Approach

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf

AI Planner Applications 8

Listed from least to most autonomous mode:

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single low-level real-time command execution

2.

time-stamped command sequence execution

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single goal achievement with auto-recovery

4.

model-based state estimation & error detection

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scripted plan with dynamic task decomposition

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• n-board back-to-back plan generation,

execution, & plan recovery

DS1 Levels of Autonomy

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Slides from DS1 Final Report

http://nmp.jpl.nasa.gov/ds1/papers.html

http://nmp-techval- reports.jpl.nasa.gov/DS1/Remote_Integrated_Report.pdf DS 1 Levels of Autonomy

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Slides from DS1 Final Report

http://nmp.jpl.nasa.gov/ds1/papers.html

http://nmp-techval- reports.jpl.nasa.gov/DS1/Remote_Integrated_Report.pdf DS 1 Systems

Planning Execution Monitoring

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf

PS = Planner Scheduler MM = Mission Manager EXEC = Executor MIR = Mode Identification and Recovery

AI Planner Applications 11

PS/MM

• generate plans on-board the spacecraft
• reject low-priority unachievable goals
• replan following a simulated failure
• enable modification of mission goals from ground

EXEC

• provide a low-level commanding interface
• initiate on-board planning
• execute plans generated both on-board and on the ground
• recognize and respond to plan failure
• maintain required properties in the face of failures

MIR

• confirm executive command execution
• demonstrate model-based failure detection, isolation, and recovery
• demonstrate ability to update on-board state via ground commands

DS1 RAX Functionality

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf MIR = Mode Identification and Recovery Module (Livingstone) MI = Mode Identification MR = Mode Recovery

DS1 Remote Agent (RA) Architecture

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf ACS = Attitude Control System NAV = Navigation IRS = Incremental Refinement Scheduler HSTS = Heuristic Scheduling Testbed System TDB = Temporal Data Base DLL = Domain Description Language DS1 Planner Architecture

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf HGA = High Gain Antenna

AI Planner Applications 14

• Final state goals
• “Turn off the camera once you are done using it”
• Scheduled goals
• “Communicate to Earth at pre-specified times”
• Periodic goals
• “Take asteroid pictures for navigation every 2 days for 2 hours”
• Information-seeking goals
• “Ask the on-board navigation system for the thrusting profile”
• Continuous accumulation goals
• “Accumulate thrust with a 90% duty cycle”
• Default goals
• “When you have nothing else to do, point HGA to Earth”

DS1 Diversity of Goals

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf ACS = Attitude Control System IPS = Ion Propulsion System

AI Planner Applications 15

• State/action constraints
• “To take a picture, the camera must be on.”
• Finite resources
• power
• True parallelism
• the ACS loops must work in parallel with the IPS controller
• Functional dependencies
• “The duration of a turn depends on its source and destination.”
• Continuously varying parameters
• amount of accumulated thrust
• Other software modules as specialized planners
• on-board navigator

DS1 Diversity of Constraints

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Slides from report by Gregory Dorain and David Kortenkamp, NASA. http://www.traclabs.com/~korten/tutorial/arc.pdf DDL = Domain Description Language ESL = EXEC Support Language

DS1 Domain Description Language

Temporal Constraints in DDL Command to EXEC in ESL

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From Bernard et al. 1998 DS1 Plan Fragment

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From Bernard et al. 1998 DS1 RA Exec Status Tool

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From Bernard et al. 1998

Timeline Applet – available real time over the web during the RAX flight experiment.

DS1 RA Ground Tools

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From Bernard et al. 1998

The flight experiments were conducted from May 17th 1999 over a 2 day period.

AI Planner Applications 20

• RAX was activated and controlled the spacecraft
• autonomously. Some issues and alarms did arise:
• Divergence of model predicted values of state of Ion

Propulsion System (IPS) and observed values – due to infrequency of real monitor updates.

• EXEC deadlocked in use. Problem diagnosed and fix

designed by not uploaded to DS1 for fears of safety of flight systems.

• Condition had not appeared in thousands of ground tests

indicating needs for formal verification methods for this type of safety/mission critical software.

• Following other experiments, RAX was deemed to have

achieved its aims and objectives.

DS1 – Flight Experiments 17 th – 21 st 1999

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Slides from DS1 Final Report

http://nmp.jpl.nasa.gov/ds1/papers.html

http://nmp-techval- reports.jpl.nasa.gov/DS1/Remote_Integrated_Report.pdf DS 1 Experiment 2 Day Scenario

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Slides from DS1 Final Report

http://nmp.jpl.nasa.gov/ds1/papers.html

http://nmp-techval- reports.jpl.nasa.gov/DS1/Remote_Integrated_Report.pdf

DS 1 Summary Objectives and Capabilities

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Steven A. Vere. Planning in time: Windows and duration for activities and goals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(3):246--267, 1983.

AI Planner Applications 23

Earlier Spacecraft Planning Applications

• Deviser
• NASA Jet Propulsion Lab
• Steven Vere, JPL
• First NASA AI Planner
• 1982-3
• Based on Tate’s Nonlin
• Added Time Windows
• Voyager Mission Plans
• Not used live

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Brian Drabble “Mission Scheduling for Spacecraft: The Diaries of T-SCHED”, Proceedings of First International Expert Planning Systems Conference, Brighton, June 1990, IEE, Savoy Place, London.

AI Planner Applications 24

Earlier Spacecraft Planning Applications

• T-SCHED
• Brian Drabble, AIAI
• BNSC T-SAT Project
• 1989
• Ground-based plan

generation

• 24 hour plan uploaded and

executed on UoSAT-II

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See applications described in course text book:

Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice. Elsevier/Morgan Kaufmann, 2004.

AI Planner Applications 25

Nonlin electricity generation turbine overhaul Deviser Voyager mission planning demonstration SIPE – a planner that can organise a …. brewery Optimum-AIV

• Integrating technologies
• Integrating with other IT systems

O-Plan various uses – see next slides Bridge Baron Deep Space 1 – to boldly go…

Some Other Practical Applications of AI Planning

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See http://www.aiai.ed.ac.uk/project/oplan All references given are in paper:

Tate, A. and Dalton, J. (2003) O-Plan: a Common Lisp Planning Web Service, invited paper, in Proceedings of the International Lisp Conference 2003, October 12-25, 2003, New York, NY, USA, October 12-15, 2003. http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003- luc-tate-oplan-web.doc

AI Planner Applications 26

O-Plan has been used in a variety of realistic applications:

• Noncombatant Evacuation Operations (Tate, et al., 2000b)
• Search & Rescue Coordination (Kingston et al., 1996)
• US Army Hostage Rescue (Tate et al., 2000a)
• Spacecraft Mission Planning (Drabble et al., 1997)
• Construction Planning (Currie and Tate, 1991 and others)
• Engineering Tasks (Tate, 1997)
• Biological Pathway Discovery (Khan et al., 2003)
• Unmanned Autonomous Vehicle Command and Control
• O-Plan’s design was also used as the basis for Optimum-AIV

(Arup et al., 1994), a deployed system used for assembly, integration and verification in preparation of the payload bay for flights of the European Space Agency Ariane IV launcher.

Practical Applications of AI Planning – O-Plan Applications

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See http://www.aiai.ed.ac.uk/project/oplan/ All references given are in paper:

Tate, A. and Dalton, J. (2003) O-Plan: a Common Lisp Planning Web Service, invited paper, in Proceedings of the International Lisp Conference 2003, October 12-25, 2003, New York, NY, USA, October 12-15, 2003. http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003- luc-tate-oplan-web.doc

AI Planner Applications 27

A wide variety of AI planning features are included in O-Plan:

Domain knowledge elicitation Rich plan representation and use Hierarchical Task Network Planning Detailed constraint management Goal structure-based plan monitoring Dynamic issue handling Plan repair in low and high tempo situations Interfaces for users with different roles Management of planning and execution workflow

Practical Applications of AI Planning – O-Plan Features

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AI Planner Applications 28

Outer HTN “human-relatable” approach Underlying rich time and resource

constraint handling

Integration with plan execution Model-based simulation and monitoring Rich knowledge modelling languages

and interfaces

Common Themes in Practical Applications of AI Planning

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AI Planner Applications 29

Summary

Deep Space 1 and Remote Agent

Experiment

Other Practical Applications of AI Planners Common Themes

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AI Planner Applications 30

Literature

Deep Space 1 Papers

• Ghallab, M., Nau, D. and Traverso, P., Automated Planning – Theory and

Practice, chapter 19,. Elsevier/Morgan Kaufmann, 2004.

• Bernard, D.E., Dorais, G.A., Fry, C., Gamble Jr., E.B., Kanfesky, B., Kurien, J.,

Millar, W., Muscettola, N., Nayak, P.P., Pell, B., Rajan, K., Rouquette, N., Smith, B., and Williams, B.C. Design of the Remote Agent experiment for spacecraft autonomy. Procs. of the IEEEAerospace Conf., Snowmass, CO, 1998.

• http://nmp.jpl.nasa.gov/ds1/papers.html

Other Practical Planners

• Ghallab, M., Nau, D. and Traverso, P., Automated Planning – Theory and

Practice, chapter 22 and 23. Elsevier/Morgan Kaufmann, 2004

• Tate, A. and Dalton, J. (2003) O-Plan: a Common Lisp Planning Web Service,

invited paper, in Proceedings of the International Lisp Conference 2003, October 12-25, 2003, New York, NY, USA, October 12-15, 2003.

• http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003-luc-tate-oplan-web.pdf

Literature

Deep Space 1 is described in Chapter 19 in the course textbook: Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice. Elsevier/Morgan Kaufmann, 2004. Further practical planning systems are described in several chapters

• f the course textbook… e.g. chapters 22 and 23.

Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning – Theory and Practice. Elsevier/Morgan Kaufmann, 2004.