___________________________________________________ Intelligent Planning and Collaborative Systems for Emergency Response .

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Intelligent Planning and Collaborative Systemsfor Emergency Response

http://i-x.infohttp://i-rescue.org

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S •More than 20 years of excellence in applied Artificial Intelligence

•World-leading AI planning research and technical team

•World-leading knowledge modelling and representation resources and staff

•O-Plan: Multi-Perspective Planning Architecture and Planning Web Service

•I-X: Issue Handling Planning and Collaboration Architecture

•<I-N-C-A>: Knowledge Elicitation, Encoding, Modelling, Representation, and Management

•I-X commercialisation through Scottish Enterprise Proof-of-Concept Award: IM-PACs

This briefing is available in http://www.aiai.ed.ac.uk/~bat/jp/

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Edinburgh AI Planners in Productive Use

http://www.aiai.ed.ac.uk/project/plan/

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I-X: Issue Handling andTask SupportArchitectureD

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Constraints

IssuesNodes

Space of Legitimate Solutions

Issues or ImpliedConstraintsNodeConstraintsDetailedConstraints

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CA=Annotations

Do (IH)

Choose (IH)

IH=Issue Handler (Agent Functional Capability)

PropagateConstraints

Planning System

Intelligent Messaging, Planning and Collaboration Systems for Emergency Response

Knowledge about places, people, processes, infrastructure, connectivity, response capabilities, and meta-knowledge

<I-N-C-A>: Knowledge Elicitation, Encoding, Modelling, Representation, and Management

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DRAFT RESPONSE PLANS: MULTIPLE COURSES OF ACTION

Effects-Oriented Planning

O-Plan/I-Plan: Multi-Perspective Planning

GOALSTASKSCOMMUNICATION

COORDINATED RESPONSE

KNOWLEDGE BASE

Shared Task and

Activity Model

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A More Collaborative & DynamicPlanning and Execution Framework

Human relatable and presentable objectives, issues, sense-making, advice, multiple options, argumentation, discussions and outline plans for higher levels

Detailed planners, search engines, constraint solvers, analyzers and simulators act as services in this framework in an understandable way to provide feasibility checks, detailed constraints and guidance

Sharing of processes and information about process products between humans and systems

Current status, context and environment sensitivity

Links between informal/unstructured sense-making and discussion and more structured planning, methods for optimisation and decision support

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I-XMulti-Agency Emergency Response Planning,

Execution, and Task-Oriented Communications

Collaboration and Communication

CommandCentre

CentralAuthorities

IsolatedPersonnel

EmergencyResponders

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<I-N-C-A> Framework

Common conceptual basis for sharing information on processes and process products

Shared, intelligible to humans and machines, easily communicated, formal or informal and extendible

Set of restrictions on things of interest:• I Issues e.g. what to do? How to do it? • N Nodes e.g. include activities or product parts• C Constraints e.g. state, time, spatial, resource, …• A Annotations e.g. rationale, provenance, reports, …

Shared collaborative processes to manipulate these:• Issue-based sense-making (e.g. gIBIS, 7 issue types)• Activity Planning and Execution (e.g. mixed-initiative planning)• Constraint Satisfaction (e.g. AI and OR methods, simulation)• Note making, rationale capture, logging, reporting, etc.

Maintain state of current status, models and knowledge I-X Process Panels (I-P2) use representation and reasoning together with

state to present current, context sensitive, options for action

Mixed-initiative collaboration model of mutually constraining things

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The I-X approach involves the use of shared models for task-directed communication between human and computer agents

I-X system or agent has two cycles:• Handle Issues• Manage Domain Constraints

I-X system or agent carries out a (perhaps dynamically determined) process which leads to the production of (one or more alternative options for) a “product”

I-X system or agent views the synthesised artifact as being represented by a set of constraints on the space of all possible artifacts in the application domain

I-X Approach

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Constraints

Issues

Nodes

Product Model

Space of Legitimate Product Models

Issues or ImpliedConstraints

NodeConstraints

DetailedConstraints

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<I-N-C-A>

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Constraints

Issues

Nodes

Product Model

Space of Legitimate Product Models

Issues or ImpliedConstraints

NodeConstraints

DetailedConstraints

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A Annotations

Do (IH)

Choose (IH)

IH=Issue Handler (Agent Functional Capability)

PropagateConstraints

I-X and <I-N-C-A>

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I-P2 aim is a Planning, Workflow andTask Messaging “Catch All”

Can take ANY requirement to:• Handle an issue• Perform an activity• Respect a constraint• Note an annotation

Deals with these via:• Manual activity• Internal capabilities• External capabilities• Reroute or delegate to other panels or agents• Plan and execute a composite of these capabilities (I-Plan)

Receives reports and interprets them to:• Understand current status of issues, activities and constraints• Understand current world state, especially status of process products• Help user control the situation

Copes with partial knowledge of processes and organisations

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Anatomy of anI-X Process Panel

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Process Panel

I-X Process Panel and Related Tools

Domain Editor

Messenger I-Plan

Map Tool

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I-Space and I-World

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Safety and Companion Robots

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e-Response Vision

The creation and use of task-centric virtual organisations involving people, government and non-governmental organisations, automated systems, grid and web services working alongside intelligent robotic, vehicle, building and environmental systems to respond to very dynamic events on scales from local to global.

Multi-level emergency response and aid systems Personal, vehicle, home, organisation, district, regional, national,

international Backbone for progressively more comprehensive aid and emergency

response Also used for aid-orientated commercial services Robust, secure, resilient, distributed system of systems Advanced knowledge and collaboration technologies Low cost, pervasive sensors, computing and comms. Changes in building codes, regulations and practices

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e-Response Relevant Technologies

Sensors and Information Gathering• sensor facilities, large-scale sensor grids• human and photographic intelligence gathering• information and knowledge validation and error reduction• semantic web and meta-knowledge• simulation and prediction• data interpretation• identification of "need"

Emergency Response Capabilities and Availability• robust multi-modal communications• matching needs, brokering and "trading" systems• agent technology for enactment, monitoring and control

Hierarchical, distributed, large scale systems• local versus centralized decision making and control• mobile and survivable systems• human and automated adjustable autonomy mixed-initiative decision making• mixed-initiative, multi-agent planning and control• trust, security

Common Operating Methods• shared information and knowledge bases• Shared standards and interlingua• shared human scale self help web sites and collaboration aids• shared standard operating procedures at levels from local to international• standards for signs, warnings, etc.

Public Education• publicity materials• self help aids• public training

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FireGrid Technologies

Maps,Models,

ScenariosSuper-real-time Simulation

Knowledge Systems, Planning & Control

Emergency Responders

Computational Grid

Tens of Thousands of Sensors & Monitors

http://firegrid.org

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FireGrid Overview

Mission statement:- …to establish a cross-disciplinary collaborative community to pursue

fundamental research for developing real time emergency response systems using the Grid…

- Initial domain is fire emergencies.

Challenges:- Sensing: instantaneous and continuous relay of data from emergency location to

response system via the Grid.- Modelling: model the evolution of fire and impact on building, and relate this to

intervention alternatives and evacuation strategies.- Forecast: all simulations, analyses and communications done in ‘super real-time’.- Response: effective co-ordination of response with intelligent decision-support system.- Feedback: continuously update simulations, predictions and response using latest data

from sensors and responders.

Status:- DTI/University of Edinburgh/Industry-funded project, total value: £2.23M, start

date: 1st March 2006.- Modelling Emergencies in Real-Time from Sensor Input (MERSI) research project

at initial (EPSRC) proposal stage.

http://firegrid.org

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The FireGrid Cluster

Universities and Colleges:

- University of Edinburgh; Imperial College London; Queen Mary, University of London; The Fire Service College, UK; Institute of High Performance Computing, Singapore; TU Delft, The Netherlands; IHMC Florida

National Research Laboratories:

- National e-Science Centre, UK; Health and Safety Laboratory, UK; NIST, USA; Major Accident Prevention Division, IRSN, France; TNO Building and Construction Research, The Netherlands.

Computational Software and Sensing Technology Companies:

- Vision Systems (Europe) Ltd.; ABAQUS UK Ltd.; ANSYS Europe Ltd.; Integrated Environment Solutions Ltd.

Engineering and Technology Consultancy Companies:

- Arup Fire; BRE Building Research Establishment Ltd.

Emergency Planning and Response:

- Fire Research Division, Office of the Deputy Prime Minister, UK; London Fire and Emergency Planning Authority; Lothian and Borders Fire Brigade, Edinburgh; Greater Manchester County Fire and Rescue Service.

http://firegrid.org

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Adapted from H. Kitano and  S. Tadokoro, RoboCup Rescue A Grand Challenge

for Multiagent and Intelligent Systems, AI Magazine, Spring, 2001.

Cycle 20

Cycle 200Blocked Roads Roads Buildings

Ambulance Team Fire Brigade Police Force

AmbulanceCentre

FireStation

PoliceOffice

Search and Rescue Command Centre

RoboCup Rescue SimulatorSimulates the Kobe earthquakeSends sensorial information to agents, receiving back action commands

I-X AgentsDivided in three hierarchical decision-making levelsSupport ideas such as activity oriented planning, coordination and knowledge sharing

Interaction I-X to Kobe SimulatorInformation from RCRS to I-X is converted to the <I-N-C-A> format

http://www.capwin.org

http://www.esa.int/navigation/galileo/

Galileo

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More Information

• www.aiai.ed.ac.uk/project/plan/

• www.aiai.ed.ac.uk/project/ix/

• i-rescue.org

• i-x.info

• i-c2.com

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Prof. Austin Tate

• Technical Director, Artificial Intelligence Applications Institute

• Professor of Knowledge-Based Systems, University of Edinburgh

• Fellow of the Royal Society of Edinburgh (Scotland's National Academy), Fellow of the American Association for AI, Fellow of the British Computer Society, Fellow of the International Workflow Management Coalition, and a member of the editorial board of a number AI journals.

• His internationally sponsored research work involves advanced knowledge and planning technologies, especially for use in emergency response and search and rescue.

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Spare Slides

Spare Slides

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High Level Planning and Activity Management

Sensors, User Inputs, E-mail, External Influences

Behaviours: Preprogramed, Situation-Response, Reactive

Sub-plan Library

HTN Planning&

<I-N-C-A>Diary

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HTN PlanningActivity Composition

A1

A2

A3

A5A4

“Initial” Plan

Refine

Introduce activities to achieve preconditionsResolve interactions between conditions and effects

Handle constraints (e.g. world state, resource, spatial, etc.)

“Final” Plan

A2.2A2.1

A1

A3

A5A4

Plan Library

A2 Refinement

S2S1

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HTN PlanningInitial Plan Stated as “Goals”

Refine

Plan Library

Ax Refinement

S2S1

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Initial Plan can be any combination of Activities and Constraints

“Refined” Plan

A1.2A1.1

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Some Planning Features

Expansion of a high level abstract plan into greater detail where necessary.

High level ‘chunks’ of procedural knowledge (Standard Operating Procedures, Best Practice Processes, Tactics Techniques and Procedures, etc.) at a human scale - typically 5-8 actions - can be manipulated within the system.

Ability to establish that a feasible plan exists, perhaps for a range of assumptions about the situation, while retaining a high level overview.

Analysis of potential interactions as plans are expanded or developed.

Identification of problems, flaws and issues with the plan. Deliberative establishment of a space of alternative options,

perhaps based on different assumptions about the situation involved, of especial use ahead of time, in training and rehearsal, and to those unfamiliar with the situation or utilising novel equipment.

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More Planning Features

Monitoring of the execution of events as they are expected to happen within the plan, watching for deviations that indicate a necessity to re-plan (often ahead of this becoming a serious problem).

Represent the dynamic state of the world at points in the plan and use this for ‘mental simulation’ of the execution of the plan.

Pruning of choices according to given requirements or constraints.

Situation dependent option filtering (sometime reducing the choices normally open to one ‘obvious’ one.

Satisficing search to find the first suitable plan that meets the essential criteria.

Heuristic evaluation and prioritisation of multiple possible choices within the constrained search space.

Uniform use of a common plan representation with embedded rationale to improve plan quality, shared understanding, etc.

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Human Approach

Previous slides describe aspects of problem solving behaviour observed in expert humans working in unusual or crisis situations.

Gary Klein, “Sources of Power”, MIT Press, 1999.

But they also describe the hierarchical and mixed initiative approach to planning in AI developed over the last 25 years.

Compendiumhttp://www.compendiuminstitute.com

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Compendiumhttp://www.compendiuminstitute.com

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<I-N-C-A> Ontology

IssuesOutstanding questions, problems or requirements (gIBIS)

Nodes E.g. activities in a process or parts in a physical product

ConstraintsCritical Constraints (shared across multiple components)Auxiliary Constraints (localised to a single component)

Annotations E.g. decision rationale and other notes