AIAI Presentation

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__ Intelligent Planning and Collaborative
Systems for Emergency Response http//i-x.info ht
tp//i-rescue.org
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Edinburgh AI Planners in Productive Use
http//www.aiai.ed.ac.uk/project/plan/
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DECISION MAKING
Intelligent Messaging, Planning and Collaboration
Systems for Emergency Response
AUTOMATED REASONING
I-X Issue Handling andTask SupportArchitecture
KNOWLEDGE MODELLING
Effects-Oriented Planning
AIAI TECHNOLOGIES
O-Plan/I-Plan Multi-Perspective Planning
ltI-N-C-Agt Knowledge Elicitation, Encoding,
Modelling, Representation, and Management
Knowledge about places, people, processes,
infrastructure, connectivity, response
capabilities, and meta-knowledge
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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
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ltI-N-C-Agt 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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I-X Approach

• 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

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ltI-N-C-Agt
Product Model
Nodes
A Annotations
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I-X and ltI-N-C-Agt
Product Model
Nodes
A Annotations
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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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I-X Process Panel and Related Tools
Process Panel
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I-Space and I-World
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I-X Modes

• Design Mode
• Supporting domain knowledge capture, modelling
  and management, generation of pre-built options,
  and identifying key tasks in the domain
• Training Mode
• What-if exercises, rehearsal, lessons-learned,
  key topics
• Response Mode
• Planning, decision support and execution

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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

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FireGrid Technologies
Tens of Thousands of Sensors Monitors
Emergency Responders
Knowledge Systems, Planning Control
Maps, Models, Scenarios
Computational Grid
Super-real-time Simulation
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FireGrid Overview
http//firegrid.org

• 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.

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The FireGrid Cluster
Other Universities
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• RoboCup Rescue Simulator
• Simulates the Kobe earthquake
• Sends sensorial information to agents, receiving
  back action commands
• I-X Agents
• Divided in three hierarchical decision-making
  levels
• Support ideas such as activity oriented planning,
  coordination and knowledge sharing
• Interaction I-X to Kobe Simulator
• Information from RCRS to I-X is converted to the
  ltI-N-C-Agt format

Adapted from H. Kitano and  S. Tadokoro, RoboCup
Rescue A Grand Challenge for Multiagent and
Intelligent Systems, AI Magazine, Spring, 2001.
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http//www.capwin.org
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Galileo
http//www.esa.int/navigation/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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