AI Planner Applications Practical Applications of AI Planners - - PDF document

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AI Planner Applications Practical Applications of AI Planners - - PDF document

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AI Planner Applications Practical Applications of AI Planners Overview Deep Space 1 Other Practical Applications of AI Planners Common Themes AI Planner Applications 2 1 Literature Deep Space 1 Papers Ghallab, M., Nau,

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AI Planner Applications

Practical Applications of AI Planners

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

AI Planner Applications 4

Deep Space 1 – 1998-2001

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

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AI Planner Applications 5

DS 1 – Comet Borrelly

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

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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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

AI Planner Applications 8

Listed from least to most autonomous mode:

1.

single low-level real-time command execution

2.

time-stamped command sequence execution

3.

single goal achievement with auto-recovery

4.

model-based state estimation & error detection

5.

scripted plan with dynamic task decomposition

6.

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

execution, & plan recovery

DS1 Levels of Autonomy

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DS 1 Levels of Autonomy DS 1 Systems

Planning Execution Monitoring

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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

DS1 Remote Agent (RA) Architecture

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DS1 Planner Architecture

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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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

DS1 Domain Description Language

Temporal Constraints in DDL Command to EXEC in ESL

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DS1 Plan Fragment DS1 RA Exec Status Tool

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DS1 RA Ground Tools

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  • 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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DS 1 Experiment 2 Day Scenario

DS 1 Summary Objectives and Capabilities

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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

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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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

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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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

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

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