Reactive Systems - PowerPoint PPT Presentation

Reactive Systems

Yolanda Gil

CS 541, Fall 2003

(Thanks to Karen Myers from SRI International)

Attack Goliath

• Gather pile of rocks
• Grasp slingshot
• Fire at giant
• Hit on the head

• Unknown how many stones
• Unknown if stones
• Unknown how many attempts
• Conditions for termination
• What if failure
• Check state

Attack Goliath

• Gather pile of rocks
• Grasp slingshot
• Fire at giant
• Hit on the head

• Embedded in the real world
• Have sensors and effectors
• Actively test the external environment
• Need to respond to events in dynamic environments
• Failure may require aborting and generating new response
• Do we need deliberate reasoning (planning)?

• Control systems
• Networks of “variables” (arcs) and “functions” (nodes)
• Reactive Action Packages (RAPs)
• Networks of “conditions” and “tasks”
• Task Control Architecture (TCA)
• Network arranged according to “vertical capabilities”
• Procedural Reasoning System (PRS)
• Integrates planning, BDI, and reactive techniques
• Anytime algorithms
• When time is short, managing what you think about
• Other approaches and issues

• RAP (http://people.cs.uchicago.edu/~firby/raps)
• Firby, J “Task Networks for Controlling Continuous Processes”, Proceedings of Artificial Intelligence Planning conference, 1994.
• TCA (http://www-2.cs.cmu.edu/afs/cs/project/TCA/release/tca.orig.html, http://www-2.cs.cmu.edu/afs/cs/project/TCA/release/tca.html)
• Simmons, R. “Structured Control for Autonomous Robots”, IEEE Transactions on Robotics and Automation, Feb 1994.
• PRS (http://www.ai.sri.com/~prs)
• Reactive reasoning and planning: an experiment with a mobile robot, M. Georgeff and A. Lansky, in Proceedings of AAAI, 1987.
• Anytime algorithms
• Zilberstein, S. “Using Anytime Algorithms in Intelligent Systems”, AI Magazine, 1996.

Control Systems: An Example (I)Control of temperature profile for a spray deposition process. Jones, P.D.A.; Duncan, S.R.; Rayment, T.; Grant, P.S. IEEE transactions on control systems technology special issue on control of industrial spatially distributed processes, Sept 2003.

Control Systems: An Example (II)Control of temperature profile for a spray deposition process. Jones, P.D.A.; Duncan, S.R.; Rayment, T.; Grant, P.S. IEEE transactions on control systems technology special issue on control of industrial spatially distributed processes, Sept 2003.

• Address problems that require a combination of:
• Coordinated activity to accomplish tasks
• Reactivity to world dynamics
• Situate control decisions within an explicit, persistent decision-making framework

• Vertical task decomposition: several task-specific modules communicate through a central control module
• Deliberation: top-down task-subtask, resolve constraints
• Central control routes messages
• Inform, query, command, monitor

• Central control traverses tree and handles messages:
• asks gait planner to traverse arc,
• gait planner asks terrain mapper for elevation map in order to take steps
• Gait planner asks leg recovery planner to place leg, move leg, move body,
• Gait planner activates monitor whether achieved position

• Ordering and temporal constraints
• Delay planning constraint: goal cannot be issued until previous task achieved
• Can do place leg planning while monitoring achieve position
• Exception handling: error recovery modules examine and modify task trees
• Eg: if position not achieved, add take steps subtask

Reason about behavior of objects

Plan changes to the world

Identify objects

Monitor changes

Build maps

Explore

Wander

Avoid objects

• Framework for symbolic reactive control systems in dynamic environments
• Eg Mobile robot control
• Eg diagnosis of the Space Shuttle’s Reaction Controls System

• Pre-compiled procedural knowledge
• BDI (Belief, Desires, Intentions) foundation
• Combines deliberative and reactive features
• Plan selection, formation, execution, sensing
• Plans dynamically and incrementally
• Integrates goal-directed and event-driven behavior
• Can interrupt plan execution
• Meta-level reasoning
• Multi-agent planning

User

Tasks

Procedures

Interpreter

Database

Intentions

World

• Contains beliefs or facts about the world
• Includes meta-level information
• Eg goal G is active

User

Tasks

Procedures

Interpreter

Database

Intentions

World

• Represent desired behavior
• Conditions over some time interval
• eg (walk a b): set of behaviors in which agent walks from a to b)

User

Tasks

Procedures

Interpreter

Database

Intentions

World

• (! P) -- achieve P
• (? P) -- test P
• (# P) -- maintain P
• (^ C) -- wait until C
• (-> C) -- assert C
• (~> C) -- retract C

• Currently active procedures
• Procedure currently being executed

User

Tasks

Procedures

Interpreter

Database

Intentions

World

• Pre-compiled procedures
• Express actions and tests to achieve goals or to react to conditions

User

Tasks

Procedures

Interpreter

Database

Intentions

World

• Environment conditions
• Purpose (goal or condition)
• applicability criteria
• Plot
• directed graph
• partially ordered conditional & parallel actions, loops
• Successful node execution by achievement of node’s goals
• If no body: primitive action

Metapredicates

• Achieve – Achieve-By {proc}
• Test – Conclude {effects}
• Wait-Until – Use-Resource
• Require-Until

Cross-Country Delivery

Cue:

(ACHIEVE (DELIVER CUSTOMER.1 GOODS.1))

(ACHIEVE

(RECORD-INVOICE

Preconditions:

CUSTOMER.1

GOODS.1

(TEST

INVOICE.1) )

(AND

(LOCATED CUSTOMER.1 CITY.2)

(LOCATED GOODS.1 CITY.1)

(DISTANCE CITY.1 CITY.2 DISTANCE.1)

(> DISTANCE.1 1000) ) )

(ACHIEVE-BY

(ACHIEVE-BY

(LOCATED

(LOCATED

Setting:

AIRCARGO.1

LANDCARGO.1

CITY.2)

CITY.2)

(TEST

SHIP-BY-AIR) )

SHIP-BY-RAIL) )

(AND

(AIR-SHIPMENT AIRCARGO.1 GOODS.1)

(LAND-SHIPMENT LANDCARGO.1 GOODS.1) ) )

Resources:

(ACHIEVE

(LOCAL-DELIVERY

- no entry -

CUSTOMER.1

GOODS.1) )

(CONCLUDE

Propertities:

(COMPLETED-INVOICE

INVOICE.1) )

(AUTHORING-SYSTEM ACT-EDITOR)

Comment:

Long distance delivery of goods to customers

New Facts & Tasks

2

(overpressurized fuel-tank)

7

(ACHIEVE (position ox-valve closed))

Act Library

1

ACT2

Cue:

Act Execution

(TEST (overpressurized tank.1))

Facts

&

Tasks

ACT1

Cue:

(ACHIEVE (position valve.1 closed))

6

External

World

8

5

Goal2

Goal3

ACT8

ACT3

3

sleeping

sleeping

(ACHIEVE

(position ox-valve closed))

ACT1

current

Fact1

ACT2

4

normal

Intention Graph

Execution Cycle

1. New information arrives that updates facts and/or tasks

2. Acts are triggered by new facts or tasks

3. A triggered Act is intended

4. An intended Act is selected

5. That intention is activated

6. An action is performed

7. New facts or tasks are posted

8. Intentions are updated

• Can include meta-level procedures
• eg: choose among multiple applicable procedures
• eg: evaluate how much more reasoning can be done within time constraints
• eg: how to achieve a conjunction or disjunction of goals

Shuttle’s RCS Malfunction Handling

RCS Controls

• Automates specification and execution of RCS malfunction procedures.
• Reacts to changes in RCS. Ensures safe operation while carrying out diagnosis and remediation procedures.

RCS Jets

Jet Fail - On

Achieve:

Position

valve.ox

closed,

Cue

Position

valve.fu

closed

Test:

Alarm sounding,

Shuttle

MESSAGES

RCS warning light on,

GPC

Achieve:

Status RCS

jet.1

is failed-on,

Notify "Thruster

jet.1

failed-on"

GPC displays

dir.1

for

jet.1

for

rcs.1

Preconditions

Test:

External

External

Test:

Test:

Direction

jet.1

is

dir.1

TASKS

FACTS

Not high-usage of

jet.1

High-usage of

jet.1

Regulator Test

Setting

Test:

Jet Fail - On

Connected

manifold.ox

to

jet.1,

FACTS

Test:

Test:

Dump Propellant

&

Connected

manifold.fu

to

jet.1,

TASKS

Not type

jet.1

vernier

Type

jet.1

vernier

BELIEFS

Connects

valve.fu

by

leg.fu

to

manifold.fu,

Connects

valve.ox

by

leg.ox

Achieve:

to

manifold.ox

,

Pressure

manifold.ox

is

pres.ox

,

Oxidizer-subsystem

ox.1

of

rcs.1

,

Executing

Pressure

manifold.fu

is

pres.fu

procedures can post

Fuel-subsystem

fu.1

of

rcs.1

,

Procedure

GOALS, FACTS, &

Part

valve.ox

of

ox.1

,

Library

BELIEFS

Part

valve.fu

of

fu.1

or

Test:

Test:

send MESSAGES

Determine new

Achieve:

> pres.ox 130,

≤ pres.ox 130,

procedures

Notify "Thruster

jet.1

failed-on

> pres.fu 130

≤ pres.fu 130

that are eligible

Jet Fail - On

ELECTRICALLY"

for execution

Regulator Test

Achieve:

Achieve:

Notify "Thruster

jet.1

failed-on

Notify "TURN-OFF rcs.1 manifold.ox

INPUT CARD"

& manifold.fu DRIVER"

Select procedures

for execution

• Multithreaded operation: multiple tasks being performed, runtime stacks where tasks are executed, suspended, and resumed
• Supports distributed planning: several PRS agents run asynchronously and communicate through message passing

• Time to deliberate about events varies
• Algorithms to compute the best answers they can in the time available
• Anytime algorithms
• Can be suspended and resumed with little overhead
• Can be terminated at any time and return some answer
• The answers returned improve with time

• Observe (O)
• React (E): time required to carry out reaction of type E
• Herald (C): earliest observation time that enables prediction of condition C requiring a response
• Utility (C,E): utility of reacting to with E to C
• Response (C): time between having information to predict C and C occurring

• Prediction time: time required to predict event given info available
• Deliberation time: max time for committing to a reaction (if reaction is needed)
• Reaction time: time required to react to event
• React(E) + Response(C)

• Decision procedure D for each C: given t time to deliberate, D returns best guess E about how to react
• Utility(C, D(C,T))
• Deliberation scheduling:
• Given several deliberation procedures, determine how to best allocate deliberation time

Linear improvement, bounded utility

One-shot improvement

Linear improvement, unbounded utility

Diminishing returns

• Blackboard architectures (Guardian)
• Universal plans
• Related issues covered in the course:
• Reasoning about uncertainty
• Learning
• from the environment
• Becoming increasingly reactive

• Control systems
• Networks of “variables” (arcs) and “functions” (nodes)
• Reactive Action Packages (RAPs)
• Networks of “conditions” and “tasks”
• Task Control Architecture (TCA)
• Network arranged according to “vertical capabilities”
• Procedural Reasoning System (PRS)
• Integrates planning, BDI, and reactive techniques
• Anytime algorithms
• When time is short, managing what you think about
• Learning and uncertainty reasoning