Intelligent Systems for Planning, Execution and CollaborationAustin TateAIAI, University of Edinburgh

Planning - Key task - List of important and varied applications - HTN framework as an integrator - Wide variety of planning techniquesExecution - USE of plans - ExamplesCollaboration - Plans to aid communications and collab.Pointer to the Future- Web + Social Networking + Agents + Plans + Virtual Worlds

Putting Plans to Use

O-Plan and its Applications

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/2000/2000-sges-tate-intelligible-planning.pdf

I-X/I-Plan and its Integration Approach

Tate, A. (2000) Intelligible AI Planning, in Research and Development in Intelligent Systems XVII, Proceedings of ES2000, The Twentieth British Computer Society Special Group on Expert Systems International Conference on Knowledge Based Systems and Applied Artificial Intelligence, pp. 3-16, Cambridge, UK, December 2000, Springer.http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003-luc-tate-oplan-web.pdf

I-Rooms

Tate, A. (2010) I-Room: Integrating Intelligent Agents and Virtual Worlds, X10 Workshop on Extensible Virtual Worlds (http://vw.ddns.uark.edu/X10). Organized by the IBM Academy of Technology and the University of Arkansas. Second Life, March 29-30, 2010.http://www.aiai.ed.ac.uk/project/ix/documents/2010/2010-xvw-tate-iroom.pdf

Helpful Environment

Tate, A. (2006) The Helpful Environment: Geographically Dispersed Intelligent Agents That Collaborate, Special Issue on "The Future of AI", IEEE Intelligent Systems, May-June 2006, Vol. 27, No. 3, pp 57-61. IEEE Computer Society.http://www.aiai.ed.ac.uk/project/ix/documents/2006/2006-ieee-is-tate-helpful-env-as-published.pdfSuggested Reading

AI PlanningPractical AI PlannersEdinburgh PlannersNonlinO-PlanOptimum-AIVI-X/I-PlanPlanning++

Edinburgh AI Planners in Productive Usehttp://www.aiai.ed.ac.uk/project/plan/

Hierarchical Task Network PlannerPartial Order PlannerPlan Space Planner (vs. Application State Space)Goal structure-based plan development - considers alternative approaches based on plan rationaleQA/ Modal Truth Criterion Condition AchievementCondition Types to limit searchCompute Conditions for links to external data and systems (attached procedures)Time and Resource Constraint checks

Nonlin core is basis for text book descriptions of HTN PlanningNonlin (1974-1977)

Domain knowledge elicitation and modelling toolsRich plan representation and useHierarchical Task Network PlanningDetailed constraint managementGoal structure-based plan monitoringDynamic issue handlingPlan repair in low and high tempo situationsInterfaces for users with different rolesManagement of planning and execution workflowO-Plan (1983-1999) FeaturesFeatures Typical of a number of Practical AI Planning Planners

O-Plan (1983-1999) Lineage

O-Plan Unix Sys Admin Aid

O-Plan Emergency ResponseTask Description,Planning and Workflow Aids

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-Plans 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

Optimum-AIV

Rich plan representation and useHierarchical Task Network PlanningDetailed constraint managementPlanner and User rationale recordedDynamic issue handlingPlan repair using test failure recovery plansIntegration with ESAs Artemis Project Management SystemOptimum-AIV (1992-4) Features

Nonlin electricity generation turbine overhaulDeviser Voyager mission planning demonstrationSIPE a planner that can organise a . breweryOptimum-AIVIntegrating technologiesIntegrating with other IT systemsO-Plan a wide range of diverse applicationsBridge BaronDeep Space 1 to boldly go

Some Practical Applicationsof AI Planning

Deep Space 1 1998-2001http://nmp.jpl.nasa.gov/ds1/

DS 1 Comet Borrellyhttp://nmp.jpl.nasa.gov/ds1/

Constraint-based planning and schedulingsupports goal achievement, resource constraints, deadlines, concurrencyRobust multi-threaded executionsupports reliability, concurrency, deadlinesModel-based fault diagnosis and reconfigurationsupports limited observability, reliability, concurrencyReal-time control and monitoring

DS1 Remote Agent Approach

Outer human-relatable approach (e.g. HTN)Underlying rich time and resource constraint handlingIntegration with plan executionModel-based simulation and monitoringRich knowledge modelling languages and interfacesCommon Themes in Practical Applications of AI Planning

Planning Research Areas & TechniquesSearch MethodsHeuristics, A*Graph Planning Algthms GraphPlanPartial-Order PlanningNonlin, UCPOPHierarchical PlanningNOAH, Nonlin, O-PlanRefinement PlanningKambhampatiOpportunistic SearchOPMConstraint SatisfactionCSP, OR, TMMSOptimisation MethodsNN, GA, Ant Colony Opt.Issue/Flaw HandlingO-Plan

Plan AnalysisNOAH, CriticsPlan SimulationQinetiQPlan Qualitative MdlingExcalibur

Plan RepairO-PlanRe-planningO-PlanPlan MonitoringO-Plan, IPEM

Plan GeneralisationMacrops, EBLCase-Based PlanningCHEF, PRODIGYPlan LearningSOAR, PRODIGYUser InterfacesSIPE, O-PlanPlan AdviceSRI/MyersMixed-Initiative PlansTRIPS/TRAINSPlanning Web ServicesO-Plan, SHOP2Plan Sharing & CommsI-X, NL GenerationDialogue ManagementDomain ModellingHTN, SIPEDomain DescriptionPDDL, NIST PSLDomain AnalysisTIMS

Planning Research Areas & TechniquesDeals with whole life cycle of plans Plan RepairO-PlanRe-planningO-PlanPlan MonitoringO-Plan, IPEM

Plan GeneralisationMacrops, EBLCase-Based PlanningCHEF, PRODIGYPlan LearningSOAR, PRODIGYUser InterfacesSIPE, O-PlanPlan AdviceSRI/MyersMixed-Initiative PlansTRIPS/TRAINSPlanning Web ServicesO-Plan, SHOP2Plan Sharing & CommsI-X, NL GenerationDialogue ManagementSearch MethodsHeuristics, A*Graph Planning Algthms GraphPlanPartial-Order PlanningNonlin, UCPOPHierarchical PlanningNOAH, Nonlin, O-PlanRefinement PlanningKambhampatiOpportunistic SearchOPMConstraint SatisfactionCSP, OR, TMMSOptimisation MethodsNN, GA, Ant Colony Opt.Issue/Flaw HandlingO-Plan

Plan AnalysisNOAH, CriticsPlan SimulationQinetiQPlan Qualitative MdlingExcalibur

Domain ModellingHTN, SIPEDomain DescriptionPDDL, NIST PSLDomain AnalysisTIMSProblem is to make sense of all these techniques

Human relatable and presentable objectives, issues, sense-making, advice, multiple options, argumentation, discussions and outline plans for higher levelsDetailed planners, search engines, constraint solvers, analyzers and simulators act in this framework in an understandable way to provide feasibility checks, detailed constraints and guidanceSharing of processes and information about process products between humans and systemsCurrent status, context and environment sensitivityLinks between informal/unstructured planning, more structured planning and methods for optimisationA More CollaborativePlanning Framework

Shared, intelligible, easily communicated and extendible conceptual model for objectives, processes, standard operating procedures and plans: IIssuesNNodes/ActivitiesCConstraintsAAnnotationsCommunication of dynamic status and presence for agents, and reports about their collaborative processes and process productsContext sensitive presentation of options for actionIntelligent activity planning, execution, monitoring, re-planning and plan repair via I-Plan and I-P2 (I-X Process Panels)I-X/I-Plan (2000- )

FrameworkCommon conceptual basis for sharing information on processes and process productsShared, intelligible to humans and machines, easily communicated, formal or informal and extendibleSet of restrictions on things of interest: IIssuese.g. what to do? How to do it? NNodese.g. include activities or product partsCConstraintse.g. state, time, spatial, resource, AAnnotationse.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 knowledgeI-X Process Panels (I-P2) use representation and reasoning together with state to present current, context sensitive, options for actionMixed-initiative collaboration model of mutually constraining things

I-P2 aim is a Planning, Workflow and Task Messaging Catch AllCan take ANY requirement to:Handle an issuePerform an activityRespect a constraintNote an annotationDeals with these via:Manual activityInternal capabilitiesExternal capabilitiesReroute or delegate to other panels or agentsPlan and execute a composite of these capabilities (I-Plan)Receives reports and interprets them to:Understand current status of issues, activities and constraintsUnderstand current world state, especially status of process productsHelp user control the situationCopes with partial knowledge of processes and organisations

Process PanelI-X Process Panel and Tools

I-X for Emergency Response

I-Room: a Virtual Space for Intelligent Interaction

Operations Centres, Brainstorming Spaces, Team Meeting Rooms, Training and Review Areas

I-Room Introduction

I-Room provides a 3D virtual space with multiple work zones, designed for collaborative and brain storming style meetingsI-Rooms are used in the I-X research on intelligent collaborative and task support environmentsThe main feature of the I-Room is the link up with external web services, collaboration systems and intelligent systems aids

I-Room Applications

Virtual collaboration centreBusiness teleconferencingTeam Meetings for project and product reviewsProduct Help DesksDesign to Product - product lifecycle supportEnvironment, building and plant monitoringHealth and safety at work, disability awarenessIntelligent tutors, guides and greetersActive demonstration pavilions

I-Room Integration

The I-Room 3D virtual space is linked to a social networking and community knowledge management web portal in OpenVCE.netRecent experimental use of the I-Room and OpenVCE for the "Whole of Society Crises Response" (WoSCR) community in the conduct of emergency response and crisis managementThis is intended as a contribution to the wider notions of "The Helpful Environment"

I-Room: Mixed-initiative Collaboration

Truly distributed mixed initiative collaboration and task support is the focus of the I-Room, allowing for the following tasks:situation monitoringsense-makinganalysis and simulationplanningoption analysisbriefingdecision makingresponsive enactment

Sensing andSituation AnalysisPlanning, EvaluationOption ArgumentationBriefing andDecision MakingActing, Reactingand CommunicationCentralMeetingArea

Tate, A. (2006) The Helpful Environment: Geographically Dispersed Intelligent Agents That Collaborate, Special IssueOn "The Future of AI", IEEE Intelligent Systems, May-June 2006, Vol. 27, No. 3, pp 57-61. IEEE Computer Society.Helpful EnvironmentThe creation and use of task-centric virtual organizations involving people, government and non-governmental organizations, 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 systemsPersonal, vehicle, home, organization, district, regional, national, internationalBackbone for progressively more comprehensive aid and emergency responseAlso used for aid-orientated commercial servicesRobust, secure, resilient, distributed system of systemsAdvanced knowledge and collaboration technologiesLow cost, pervasive sensor grids, computing and communicationsChanges in codes, regulations, training and practices

O-Plan and its Applications

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/2000/2000-sges-tate-intelligible-planning.pdf

I-X/I-Plan and its Integration Approach

Tate, A. (2000) Intelligible AI Planning, in Research and Development in Intelligent Systems XVII, Proceedings of ES2000, The Twentieth British Computer Society Special Group on Expert Systems International Conference on Knowledge Based Systems and Applied Artificial Intelligence, pp. 3-16, Cambridge, UK, December 2000, Springer.http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003-luc-tate-oplan-web.pdf

I-Rooms

Tate, A. (2010) I-Room: Integrating Intelligent Agents and Virtual Worlds, X10 Workshop on Extensible Virtual Worlds (http://vw.ddns.uark.edu/X10). Organized by the IBM Academy of Technology and the University of Arkansas. Second Life, March 29-30, 2010.http://www.aiai.ed.ac.uk/project/ix/documents/2010/2010-xvw-tate-iroom.pdf

Helpful Environment

Tate, A. (2006) The Helpful Environment: Geographically Dispersed Intelligent Agents That Collaborate, Special Issue on "The Future of AI", IEEE Intelligent Systems, May-June 2006, Vol. 27, No. 3, pp 57-61. IEEE Computer Society.http://www.aiai.ed.ac.uk/project/ix/documents/2006/2006-ieee-is-tate-helpful-env-as-published.pdfSuggested Reading

I-X Intelligent Systems TechnologyI-Room a Virtual Space for Intelligent InteractionOpenVCE Virtual Collaboration EnvironmentThe Helpful Environmenthttp://i-x.infohttp://openvce.nethttp://openvce.net/i-roomhttp://openvce.net/helpful-environmentWeb + Social Networking + Agents + Plans + Virtual Worlds

Extra SlidesDeep Space 1 Extra Slides and PapersI-X Extra SlidesI-Room Extra SlidesHelpful Environment Extra Slides

Achieve diverse goals on real spacecraftHigh Reliabilitysingle point failuresmultiple sequential failuresTight resource constraintsresource contentionconflicting goalsHard-time deadlinesLimited ObservabilityConcurrent Activity

DS1 Domain Requirements

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 Experiments17th 21st May 1999

DS1 LiteratureDeep Space 1 PapersGhallab, 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 PlannersGhallab, M., Nau, D. and Traverso, P., Automated Planning Theory and Practice, chapter 22 and 23. Elsevier/Morgan Kaufmann, 2004Tate, 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

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 IssuesManage 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 artefact as being represented by a set of constraints on the space of all possible artefacts in the application domainI-X Approach

Helpful Environment Related Projects CoAKTinG (Collaborative Advanced Knowledge Technologies in the Grid) also I-Rescue (Kobe), AKT e-Response and OpenKnowledgeLinking issue handling, argumentation, process support, instance messaging and agent presence notificationRange of natural, industrial and other emergency scenarios

CoSAR-TS (Coalition Search and Rescue Task Support)Use of OWL ontologies and OWL-S described services to describe components

Co-OPR (Collaborative Operations for Personnel Recovery)Use of OWL ontologies and OWL-S described services to describe components

FireGridto establish a cross-disciplinary collaborative community to pursue fundamental research for developing faster than real time emergency response systems using the Grid

e-ResponseCreation and use of task-centric virtual organizations to respond to highly dynamic events on scales from local to globalFlood, metropolitan emergency and industrial accident scenarios

"The Helpful Environment" vision is of a future in which ubiquitous computing, sensor grids and networked systems combine to help the individuals, families, businesses, organizations, the public at large, regions and countries to be self supportive and mutually helpful with specialised resources for their daily lives, for help and assistance in emergencies.

The vision, some international programmes which contribute to it, some of the organisations that are pursuing this vision and some of the Edinburgh projects and research that will we hope will help make it a reality is described in this paper:

Tate, A. (2006) The Helpful Environment: Geographically Dispersed Intelligent Agents That Collaborate, Special IssueOn "The Future of AI", IEEE Intelligent Systems, May-June 2006, Vol. 27, No. 3, pp 57-61. IEEE Computer Society.Helpful Environment

Adapted from H. Kitano and S. Tadokoro, RoboCup Rescue A Grand Challengefor Multiagent and Intelligent Systems, AI Magazine, Spring, 2001. RoboRescue 50 Year Programme

AIAI, University of EdinburghIntelligent Systems - Planning and Activity Management

Explores representations and reasoning mechanisms for inter-agent activity support. The agents may be people or computer systems working in a coordinated fashion. The group explores and develops generic approaches by engaging in specific applied studies. Applications include crisis action planning, command and control, space systems, manufacturing, logistics, construction, procedural assistance, help desks, emergency response, etc.

Our long term aim is 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.

http://www.aiai.ed.ac.uk/project/plan/ AIAI Planning Group Aims

**Putting Plans to Use - Intelligent Systems for Planning, Execution and Collaboration

Talk Outline

PlanningKey taskList of important and varied applicationsHTN framework as an integratorList of planning techniques

Execution - USE of plansExamples

CollaborationPlans to aid communications and collababoration

Pointer to the FutureWeb + Social Networking + Agents + Plans + Virtual Worlds

Prof. Austin TateDirector, AIAI, University of EdinburghProfessor of Knowledge-Based Systems, University of EdinburghCoordinator, Virtual University of EdinburghFellow of the Royal Society of EdinburghFellow of the Association for the Advancement of Artificial IntelligenceAdvisory Board Member, IEEE Intelligent Systems

Putting Plans to Use - Intelligent Systems for Planning, Execution and Collaboration

Planning is a key task of importance to governments and organisations of all kinds, and to us all on a personal, family and business level. It is one of the most studied areas in Artificial Intelligence and one that demands many aspects and approaches. It has vital and important applications in a wide variety of areas. We will start by exploring a few applications where AI planning technology is in regular and productive use.

A successful paradigm for real planning applications has been to use a framework of hierarchical task network (TN) planning into which a number of other more specialised and search and constraint management methods are added. A lot of academic research to date has been very narrowly focused and does not attempt a broader integration of a range of separately successful methods. We will rapidly review the breadth and openness to new exploration of this exciting field.

But planning is only useful if plans are successfully executed. This is tricky. The world is very messy when you try to model, predict, plan and interact with it. We will explore an application of AI planning that executed in deep space far far away from immediate human assistance. Was it successful? We will see.

Planning in a careful modelled domain where things are quite predicable is one thing, but the real world of planning involves a wide range of agents, humans, disparate teams from widely differing background and cultures, robots that interact with physical complex environment, and computer and communications systems that do not always work in an ideal way. But plans can be used to assist in communication and collaboration, including in some of the most dynamic and challenging applications such as international emergency response.

Lets look forward to a future "Helpful Environment" aided by AI planning and knowledge-based technologies - a future in which ubiquitous computing, sensor grids and networked systems combine to help the individuals, families, businesses, organizations, the public at large, regions and countries to be self supportive and mutually helpful with specialised resources for their daily lives, for help and assistance in emergencies. This will demand a new generation of researchers and entrepreneurs able to combine the next generation Internet, semantic web, social networking, agent-based technologies, intelligent planning systems and virtual environments into a new approach. It will be quite a journey.

Reading

O-Plan and its Applications

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/2000/2000-sges-tate-intelligible-planning.pdf

I-X/I-Plan and its Integration Approach

Tate, A. (2000) Intelligible AI Planning, in Research and Development in Intelligent Systems XVII, Proceedings of ES2000, The Twentieth British Computer Society Special Group on Expert Systems International Conference on Knowledge Based Systems and Applied Artificial Intelligence, pp. 3-16, Cambridge, UK, December 2000, Springer.http://www.aiai.ed.ac.uk/project/ix/documents/2003/2003-luc-tate-oplan-web.pdf

I-Rooms

Tate, A. (2010) I-Room: Integrating Intelligent Agents and Virtual Worlds, X10 Workshop on Extensible Virtual Worlds (http://vw.ddns.uark.edu/X10). Organized by the IBM Academy of Technology and the University of Arkansas. Second Life, March 29-30, 2010.http://www.aiai.ed.ac.uk/project/ix/documents/2010/2010-xvw-tate-iroom.pdf

Helpful Environment

Tate, A. (2006) The Helpful Environment: Geographically Dispersed Intelligent Agents That Collaborate, Special Issue on "The Future of AI", IEEE Intelligent Systems, May-June 2006, Vol. 27, No. 3, pp 57-61. IEEE Computer Society.http://www.aiai.ed.ac.uk/project/ix/documents/2006/2006-ieee-is-tate-helpful-env-as-published.pdf

* *1975 Nonlin UK CEGB - Electricity Turbine Overhaul Procedures

1982 Deviser based on Nonlin NASA JPL Voyager Mission Planning

1996 OPTIMUM_AIV based on O-Plan ESA Ariane IV AIV for payload bay

1996-present Search and Rescue UK RAF and USA JPRA

Commercial applications for Nynas tanker scheduling and Edify for financial help desks

* *O-Plan has been used for a range of practical and research tasks. It was developed form 1983 to 1999 and ran as a web service fro another decade.* *Hewlett Packard requirement to handle generation of scripts for problematic and error prone UNIX systems admin task. Universal and long case-based scripts caused errors. Generative AI Planning offered a complete and verifiable solution which was also acceptable at the human operator level.* Mixed-initiative task assignment and planning with multiple distributed users supported by the automated planning aid.*See http://www.aiai.ed.ac.uk/project/oplan

All references given are in paper:

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*Optimum-AIV is in use by the European Space Agency (ESA) for Assembly, Integration and Verification (Test) planning for the Ariane Rocket payload bay. Its design was based on O-Plan algorithms and earlier development of an AI planner for the European Space Agencys ERS-1 Spacecraft (PlanERS-1)

* Note the similarity to the AI features used in O-Plan.*See applications described in the AI Planning text book:Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning Theory and Practice. Elsevier/Morgan Kaufmann, 2004.

*NASAs Deep Space 1 (DS1)http://nmp.jpl.nasa.gov/ds1/

Deep Space 1 launched from Cape Canaveral on October 24, 1998. During a highly successful primary mission, it tested 12 advanced, high-risk technologies in space. In an extremely successful extended mission, it encountered Comet Borrelly and returned the best images and other science data ever from a comet. During its fully successful hyperextended mission, it conducted further technology tests. The spacecraft was retired on December 18, 2001.

IPS = Ion Propulsion System (one of the advanced technologies being demonstrated on DS1)*NASAs Deep Space 1 (DS1) at Comet Borrellyhttp://nmp.jpl.nasa.gov/ds1/*Slides from report by Gregory Dorain and David Kortenkamp, NASA.http://www.traclabs.com/~korten/tutorial/arc.pdf

**There are many techniques which together offer support for rich planning systems.*Issue is to have a framework that allows many of these techniques to co-exist and be used in a sensible way.

* ******OpenVCE.net WoSCR Community Experiment in Progress Feb 2010***I-Room on http://OpenVCE.net**I-Room arranged as 4 work areas around a central meeting area, specialised to support a range of functions in decision making, e.g. for operations centres.**OpenVCE web portal showing support for Virtual Collaboration protocol alongside virtual 3D meeting space**Experimental 3D visualisations for Questions-Options-Criteria (QOC) and Concept Maps (Cmaps).*Tate, A. (2006) The Helpful Environment: Geographically Dispersed Intelligent Agents That Collaborate, Special IssueOn "The Future of AI", IEEE Intelligent Systems, May-June 2006, Vol. 27, No. 3, pp 57-61. IEEE Computer Society.***Slides from report by Gregory Dorain and David Kortenkamp, NASA.http://www.traclabs.com/~korten/tutorial/arc.pdf

*From Bernard et al. 1998The flight experiments were conducted from May 17th 1999 over a 2 day period.*Literature

Deep Space 1 is described in Chapter 19 in the course textbook: Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning Theory and Practice. Elsevier/Morgan Kaufmann, 2004.

Further practical planning systems are described in several chapters of the course textbook e.g. chapters 22 and 23.

Malik Ghallab, Dana Nau, and Paolo Traverso. Automated Planning Theory and Practice. Elsevier/Morgan Kaufmann, 2004.****Long term programmes of R&D to address disaster areas such as RoboRescue 50 year programme in Japan.**More details at http://www.aiai.ed.ac.uk/project/plan/