1 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications A Shared Model for Mixed-initiative Synthesis Tasks.

Slides:



Advertisements
Similar presentations
Semantics for e-Science Collaboration Jan Update.
Advertisements

1 CoAKTinG – I-X Process Panels I-Space: I-Me, I-Room, I-World I-Tools: I-DE, I-Think, I-Plan, I-AM AIAI, University of Edinburgh
Outbrief of SWSI Architecture Committee F2F Sat, April 12, 2003 Miami, FL Mark H. Burstein BBN Technologies.
Abstraction Layers Why do we need them? –Protection against change Where in the hourglass do we put them? –Computer Scientist perspective Expose low-level.
Planning in Context Planning in the Context of Domain Modelling, Task Assignment and Execution
1 Artificial Intelligence Applications Institute, University of Edinburgh, UK Institute for Human and Machine Cognition, Pensacola, Florida AI Planning.
1 Artificial Intelligence Applications Institute, University of Edinburgh Institute for Human & Machine Cognition, University of West Florida CoSAR-TS.
1 Artificial Intelligence Applications Institute, University of Edinburgh Institute for Human & Machine Cognition, University of West Florida CoSAR-TS.
___________________________________________________ Intelligent Planning and Collaborative Systems for Emergency Response
2 Artificial Intelligence Applications Institute, University of Edinburgh, UK Institute for Human and Machine Cognition, Pensacola, Florida CoSAR-TS Coalition.
Shared Models of Activity To Underpin Small Unit Operations Austin Tate, Jeff Dalton, John Levine & Peter Jarvis Artificial Intelligence Applications Institute.
1 Stephen Potter University of Edinburgh Building Collaborative eResearch Environments Virtual Organisations and Collaborative Environments.
Simon Buckingham Shum David De Roure Marc Eisenstadt Nigel Shadbolt Austin Tate.
I-Room : Integrating Intelligent Agents and Virtual Worlds.
Supporting the Requirement for Flexibility in Automated Business Processes using Intelligent Agents Stewart Green University of the West of England.
Introduction to Web services MSc on Bioinformatics for Health Sciences May 2006 Arnaud Kerhornou Iván Párraga García INB.
MACMERL Mixed-Initiative Scheduling with Coincident Problem Spaces M.J. Prietula, W.L. Hsu, P.S.Ow.
Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Improving Robustness in Distributed Systems Jeremy Russell Software Engineering Honours Project.
(Re)Designing Software Production Architectures Walt Scacchi ATRIUM Laboratory and USC Center for Software Engineering 10.
Community Manager A Dynamic Collaboration Solution on Heterogeneous Environment Hyeonsook Kim  2006 CUS. All rights reserved.
Process-oriented System Automation Executable Process Modeling & Process Automation.
GMD German National Research Center for Information Technology Innovation through Research Jörg M. Haake Applying Collaborative Open Hypermedia.
ONTOLOGY SUPPORT For the Semantic Web. THE BIG PICTURE  Diagram, page 9  html5  xml can be used as a syntactic model for RDF and DAML/OIL  RDF, RDF.
Knowledge Management in Geodise Geodise Knowledge Management Team Liming Chen, Barry Tao, Colin Puleston, Paul Smart University of Southampton University.
1 Semantic-Based Workflow Composition for Video Processing in the Grid Gayathri Nadarajan, Yun-Heh Chen-Burger, James Malone Centre for Intelligent Systems.
 Cloud computing  Workflow  Workflow lifecycle  Workflow design  Workflow tools : xcp, eucalyptus, open nebula.
Katanosh Morovat.   This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation.
Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves.
Brussels, 04 March 2004Workshop „New Communication Paradigms for 2020“ Semantic Routing, Service Discovery and Service Composition Gregor Erbach German.
An approach to Intelligent Information Fusion in Sensor Saturated Urban Environments Charalampos Doulaverakis Centre for Research and Technology Hellas.
CoAKTinG Concepts from Edinburgh Austin Tate, AIAI, 21-Mar-2002 First Ideas… Strong issues, activities/processes, state, event, agents, options, argumentation,
European Network of Excellence in AI Planning Intelligent Planning & Scheduling An Innovative Software Technology Susanne Biundo.
CONTENTS Arrival Characters Definition Merits Chararterstics Workflows Wfms Workflow engine Workflows levels & categories.
University of Sunderland COMM80 Risk Assessment of Systems ChangeUnit 13 Overview of Riskit*: The Method and its Techniques * Further information available.
Lecture 9: Chapter 9 Architectural Design
Task Achieving Agents on the World Wide Web An Introduction Sharif Univ. of Tech. Computer Eng. Dep. Semantic Web Course Mohsen Lesani 13 Ord 1374.
SUO Planning & Decision Aids Austin Tate, AIAI, University of Edinburgh David Wilkins, SRI International Capability to communicate, refine, execute and.
Issues in (Financial) High Performance Computing John Darlington Director Imperial College Internet Centre Fast Financial Algorithms and Computing 4th.
Carnegie Mellon Interactive Resource Management in the COMIREM Planner Stephen F. Smith, David Hildum, David Crimm Intelligent Coordination and Logistics.
I-Room: a Virtual Space for Intelligent Interaction Low cost, simple setup, mixed-reality meetings spaces and operations centres
©Ferenc Vajda 1 Semantic Grid Ferenc Vajda Computer and Automation Research Institute Hungarian Academy of Sciences.
1 USC INFORMATION SCIENCES INSTITUTE CAT: Composition Analysis Tool Interactive Composition of Computational Pathways Yolanda Gil Jihie Kim Varun Ratnakar.
© Geodise Project, University of Southampton, Knowledge Management in Geodise Geodise Knowledge Management Team Barry Tao, Colin Puleston, Liming.
Cooperative experiments in VL-e: from scientific workflows to knowledge sharing Z.Zhao (1) V. Guevara( 1) A. Wibisono(1) A. Belloum(1) M. Bubak(1,2) B.
Practical HTN Planning Putting HTN Planning into Use.
___________________________________________________ Intelligent Planning and Collaborative Systems for Emergency Response
1 Centre for Intelligent Systems and their Applications Division of Informatics, University of Edinburgh Draft for AKT July Workshop Jessica Chen-Burger.
MODEL-BASED SOFTWARE ARCHITECTURES.  Models of software are used in an increasing number of projects to handle the complexity of application domains.
© The ATHENA Consortium. EM1 - Enterprise Modelling as a way to achieve Interoperability Module 3 - What interoperability problems does Enterprise.
16/11/ Semantic Web Services Language Requirements Presenter: Emilia Cimpian
Support for cooperative experiments in VL-e: from scientific workflows to knowledge sharing.
A Mediated Approach towards Web Service Choreography Michael Stollberg, Dumitru Roman, Juan Miguel Gomez DERI – Digital Enterprise Research Institute
LQCD Workflow Project L. Piccoli October 02, 2006.
‘Activity in Context’ – Planning to Keep Learners ‘in the Zone’ for Scenario-based Mixed-Initiative Training Austin Tate, MSc in e-Learning Dissertation.
 An Information System (IS) is a collection of interrelated components that collect, process, store, and provide as output the information needed to.
1 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications IJCAI-03 MIIS Panel Communication and Awareness.
SSE3 Knowledge mangement concepts 1. Agenda What is knowledge management Classification of knowledge Knowledge management process Common/shared information.
SAGE Nick Beard Vice President, IDX Systems Corp..
___________________________________________________ Informatics MSc Course PLAN – Automated Planning The aim of this course is to provide: a solid grounding.
1 Aberdeen/Edinburgh AKT TIE Distributed Knowledge-based Workflow and Constraint Solving Jessica Chen-Burger AIAI, University of Edinburgh Kit Ying Kui.
Jessica Chen-Burger Aberdeen/Edinburgh AKT TIE Distributed Knowledge-based Manipulation and Collaboration Jessica Chen-Burger AIAI, University of Edinburgh.
A Mixed-Initiative System for Building Mixed-Initiative Systems Craig A. Knoblock, Pedro Szekely, and Rattapoom Tuchinda Information Science Institute.
I-Room: a Virtual Space for Intelligent Interaction
Activity in Context – Planning to Keep Learners ‘in the Zone’ for Scenario-based Mixed-Initiative Training Austin Tate MSc in e-Learning.
<I-N-C-A> and the I-Room
I-X Austin Tate, Jeff Dalton, Robert Inder, John Levine,
Workshop Organization Support SAR Environment Schematic
Co-OPR – Compendium and I-X
Architecture Issue in the New Disciple System
Presentation transcript:

1 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications A Shared Model for Mixed-initiative Synthesis Tasks A Shared Model for Mixed-initiative Synthesis Tasks Austin Tate Artificial Intelligence Applications Institute University of Edinburgh

2 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications I-Technology Cooperation and Communication I-DE Domain Editor I-P 2 Process Panels I-Plan/O-Plan Planning Aid CoABS Grid, KAoS, AKTBus, XML Sockets, Jabber, [Globus GT3] Other Agents & Services I-Q Adaptor

3 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications I-X Approach l The I-X approach involves the use of shared models for task-directed communication between human and computer agents who are jointly exploring (via some process) a range of alternative options for the synthesis of an artifact such as a design or a plan (termed a product). l I-X system or agent views the product as being represented by a set of constraints on the space of all possible products in the application domain. l I-X system or agent is viewed as having two cycles: –Handle Issues –Manage Domain Constraints l Agents can use their capabilities to handle issues or carry out given tasks where “authorised” to do so. l Mixed initiative model for each agent of “mutually constraining the space of products”.

4 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Ontology Ontology Issues Nodes E.g. activities in a process or parts in a physical artifact Constraints Critical Constraints (shared across multiple components) Auxiliary Constraints (localised to a single component) Annotations E.g. decision rationale and other notes

5 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Uses of a Shared Model Formal Analysis System Manipulation Knowledge Acquisition User Communication

6 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Terminology to Support Mixed-Initiative Working l Example use in TRAINS   O-Plan l Processes -- l Products -- l Options for Products l Levels within Product Descriptions l Parts (or Phases) within Product Descriptions

O-Plan Web Server Multi-user Task & Planning Support

8 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications I-X Process Panels

9 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Aim is a Workflow and Messaging “Catch All” l Can take ANY requirement to: –Handle an Issue –Perform an Activity –Respect a Constraint –Note an Annotation l Deals with these via: –Internal capabilities –External capabilities –Manual activity –Reroute or delegate to other panels or agents –Plan and execute a composite of these capabilities l Receives reports and interprets them to: –Understand current status of issues, activities, constraints & annot. –Understand current world state, especially status of process products –Help user control the situation l Copes with partial knowledge

I-X Process Panels

Process Panel Domain Editor Activity Editor Messenger I-Space Map View 3D View PDA View

12 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications l l Further Information

13 Artificial Intelligence Applications Institute Centre for Intelligent Systems and their Applications Tasks & Initiative l Strategic –Tasks: objective setting, option analysis and decision making –Task Types: analysis, direction –Aim: What to do? –Initiative (in many of our scenarios): Often manual l Tactical –Tasks: planning, scheduling, option generation –Task Types: synthesis –Aim: How to do? –Initiative: Greatest opportunity for mixed-initiative l Operational –Tasks: Enactment, adjust approach in context, select from options –Task types: execution, selection, modification –Aim: Just do it! –Initiative: Some parts may be automated