Automated Planning as a Semantic Technology

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Configuring the CloudAutomated Planning as a Semantic

Technology

Blazej Bulka, PhDClark & Parsia, LLC

[email protected]

Stuart CharltonElastra

[email protected]

2010 Semantic TechnologyConference

Who we are?

Clark & Parsia is a semantic software startup

Offices in DC and Cambridge, MA

Software products for end-user and OEM use

Provides software development and integration services

Specializing in Semantic Web, web services, and advanced AI technologies for federal and enterprise customers

Elastra is a cloud computing software startup

Offices in San Francisco, CA

Co-Funded by Amazon.com, Bay Partners, and Hummer Winblad

Provides software and services for helping organizations migrate and manage their applications in private and public clouds

Elastra Cloud Server available for Amazon Web Services and VMWare

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Outline

Introduction to automated planning Examples of planning systems Hierarchical planning Case Study: Planning in Cloud Computing Semantic technologies in planning:

HotPlanner from Clark & Parsia Conclusion

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Introduction to automated planning

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What is automated planning?

Automated process to determine which actions need to be taken to achieve a desired goal

Current state of the world Description of actions Goals and constraints

Planning system

Required actions (plan)

Multiple applications of produced plans− Software execution− Human execution− Assistance in solving a problem

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Simple exampleInstalling new software package

Current state of the world: Description of installed software packages and their dependencies

Available actions:

− Retrieve list of packages available for install from a server− Retrieve package dependencies− Download software package− Install software package

Goal: Install software package X

Plan – sequence of actions specifying which packages to download and install, and in which order

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Domain-dependent planning

Solves one type of problem well Specialized algorithm and data representation

(the designer encoded the structure of the problem and solution in the code)

Small modifications to problem (e.g., new kinds of dependencies between software packages) = modifications to the planning system

Larger modifications to problem = rewriting significant portions of the planning system

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Domain-independent planning Solves problems in multiple, entirely different

domains (e.g., software management, truck routing, space mission control)

Domain (actions, state of the world, goals) have to be specified in a way understandable to the planning system

Generic planning algorithm Modifications to planning problem (small or

large) – modifications to the domain specification = No change of planning system's code

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What makes a plan good?

Depends on the application Typical quality metrics

− Plan length (number of actions)− Makespan (time to execute the plan)− Plan cost (every action has a cost associated

with it)− Multi-objective metrics

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Examples of planning systems

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Space Exploration:Deep Space Network

Deep Space 1 (DS1)− First ever close pictures of a comet

(Borelly in 1999)

− Part of Deep Space Network

− Proof-of-concept for later systems

Benefits− Spacecraft autonomy

− Commands = goals

− Plan verification

− Execution monitoring

Images: nmp.nasa.gov/ds1/

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Earth Observing-1 Mission (EO-1)

Proof-of-concept for autonomous sensor web (incl. satellites, buoys etc.)

Autonomous analysis of data

Feature detection (e.g., fire) and decide to investigate on its own

Collaboration among multiple satellites

Plan activities: transmissions, power usage, set-up, camera usage

Dramatic increase in amount of scientifically usable data (1000 x ?)

Images: eo1.gsfc.nasa.gov

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Mars Exploration Rovers and MAPGEN

Autonomous execution of plans prepared on Earth

Very limited planning on Mars

MAPGEN – computer planning system assisting human planners

Can produce explanations of dependencies

Successor of MAPGEN is EUROPA (Extendable Uniform Remote Operations Planning Architecture)

Images: www.nasa.gov

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Xerox RMP

Planning system for Rack Mounted Printing prototype

− System with multiple parallel printing engines (for performance and fault tolerance)

− Sheet path planning (and re-planning in case of failure)

Images: Do, Ruml, and Zhou (ICAPS 2008 Proceedings)

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Planning in games

Strategy games and first-person shooters− Killzone 2 (Game of the Year 2009 in

Shooter category) 500 plans per second

− F.E.A.R. and Condemned: Criminal Origins

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Siemens Tecnomatix 9CAM-oriented planning tasks

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Hierarchical planning

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Hierarchical planning

HTN (Hierarchical Task Network) planning Hierarchy of actions/goals Hint on the problem structure from the domain expert Prevents reinventing the wheel by the planning system Planner can first solve high-level goals and then refine

lower-level ones Significant improvement in performance Designed templates of desired solutions (e.g., plans

that adhere to internal policies)

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Example HTN Plan

Goal: Have package X

Install package X

Download package list

Satisfy dependencies

for X

Goal: Have package X

Download X

InstallX

Have package Y

Have package Z

Install package Y

Satisfy dependencies

for Y

Download Y

InstallY

Already installed

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Case Study: Configuring the CloudWith Elastra Enterprise Cloud Server

Elastra Enterprise Cloud Server

• Configure, Deploy, and Scale Multi-Tier Applications» Commercial and/or open source software

• Manage Applications Across a Hybrid Cloud» VMWare, Amazon Web Services, others

• Portable & Consistent Cloud Application Management

Federated Identity & Access Control

Application-Level Automation OS-Level AutomationMonitoring & Capacity

EventCorrelation Systems

Monitoring Systems

Configuration Agents

VirtualizationManagers

Public Cloud APIs3rd Party IT Systems

Integrate

DiscoverManageIntegratePublish

Subscribe

Automated Application Deployment & Scale OutPlanning & Orchestration

PDP PEP

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The Elastic Modeling Languages:OWL Ontologies for Cloud Computing

Meeting the Challenges of IT Automation:How can these two servers communicate?

Possible areas of problems:• Security

» Bad credentials• Server Configuration

» Wrong IP or Port» Bad setup to listen or call

• Network Configuration» Wrong duplex» Bad DNS or DHCP

• Firewall Configuration» Ports or protocols not open

How do we usually automate a configuration?

• Scripts & Runbooks

• In Situation X, do this• In Situation Y, do this• In Situation Y, but exception Z, do this• ….• Problems: Context, Variability, Timing & Transitions• Scripts require you to explicitly write out each control

transition, assume you know in advance the one right way to do things

• That’s fine in the small; in the large, it gets complicated.

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Elastra Next Generation Automation Engine:

Elastra Plan Composer

HTN Strategies

Strategy Examples:Configure Full SystemScale-Out a SystemRecover a Database

HTN TacticsSearch for Next Tactics :Set Firewall RulesRestore FilesystemInstall & Start ServiceProvision Network

HTN AtomicActions

Search for Atomic Bindings:Configuration Agent CalloutCloud API CalloutVirtualization Manager Callout

Application& Deployment

Data

(BackgroundIndividuals)

ElasticModeling

Languages(OWL v2 Ontology)

Clark & Parsia HotPlanner

Clark & Parsia Pellet

Jena + SPARQL

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Elastra’s Success Results with HotPlanner

• Reduced, More Maintainable Codebase» Custom SPARQL and Java Code to interpret RDF, vs.

HotPlanner’s Domain-Specific Language for HTN» Up to a 3x reduction in code length for various modules

• Performance» Ability to generate a plan in under a minute for a 20 server, 20

component, multi-tier system design» Typical planning time: 3 to 5 seconds, up to 15 for complex» Near-equivalent to classic manual SPARQL-based analyzer

• Visibility and Modifiability of Plan» Plans are just OWL-S processes, easy to parse, render to GUI,

and/or modify before execution

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Semantic Technologies in Planning

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Semantic technologies in planning

Semantic technologies have not been used together with planning frequently so far

− Promising opportunity Modern semantic technologies (RDF and

OWL) are relatively new compared to planning Planning finally became usable in real-world

settings− The two areas can contribute to each other− Many places where one area's weakness is

the other's strength

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Expressing planning domain

Planners depend on accurate formal description of planning domain

− Language to describe the current state of the world

− Goals

− Actions Parameters (e.g., install package X) Preconditions (when an action may be executed) Effects (what changes in the world when the action is executed) Hierarchy

Ideal solution− Expressive (e.g., describing quantities)

− Keep computational complexity under control

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Classical planning formalisms are inadequate

Traditional formalisms− STRIPS, PDDL (Planning Domain Definition

Language)− Limited expressivity

Expressing complex domains difficult− Support for hierarchical planning is sparse

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Representing planning knowledge in OWL

Describing the current state of the world in RDF and OWL

− Expressive language− Designed with computational complexity in

mind (e.g., various profiles EL, RL, QL)− Can be used to infer information about the

current state of the world that is not explicitly specified

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Describing actions and hierarchies based on OWL-S

OWL-S is designed to specify descriptions of Web services

Can be used to specify any action in Hierarchical Planning by using owls:Process

− Composition of actions (hierarchy)− Preconditions− Effects− Parameters of actions

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Challenges

OWL ontologies are designed to represent static data

Planning includes reasoning about changes− Describing effects of action (some axioms

become true, other become false)− Identifying invariants that are preserved− Matching effects of one actions with

preconditions of others

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HotPlanner Clark & Parsia's response to these challenges Used in production systems Uses Pellet OWL reasoner for state

knowledge base management, reasoning and query answering– Can plan and optimize for multiple objectives

– Extension to OWL-S for actions

– Scheduling and planning for parallel task execution

For more information go to http://clarkparsia.com/planner

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Conclusions

Planning is a proven technology for task automation, decision support, autonomous systems

Semantic technologies and planning have a lot to offer to each other

Automated planning is quickly growing in use for Cloud Computing automation

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Thank you for your attention!Questions?