The SWRLing Future of OWL. Mark Greaves DARPA / IXO

Transcription

1 The SWRLing Future of OWL Mark Greaves DARPA / IXO

2 In the Beginning DARPA Agent Mark Up Language (DAML) PE/Project Number: 62301E/ST-11 PAD ID NUMBER: PGM NUMBER: XAM8E DIRO APPROVAL: PGM MANAGER: James Hendler APPROVAL DATE: PROGRAM GOALS: The DARPA Agent Mark Up Language program is focused on creating the technologies required to seed the next major evolution of the World Wide Web (W3) the ability for autonomously operating intelligent software programs (software agents) to perform distributed computing on the same worldwide scale that has been achieved in sharing information. The vision of this program is to create an environment in which intelligent distributed computing can be made as easy and ubiquitous as data exchange has become on the W3. The accomplishment of this overarching objective will require key technology advancements. We must push the development of the technological foundations that allow software agents to inter-operate with other software agents and with other entities in cyberspace such as servers, databases, legacy systems, sensors, and, we must create technologies that enable software agents to identify, communicate with, and understand other software agents dynamically (i.e., on the fly at run time, not built in at the development time). These goals will be pursued as the major thrust of the DAML program. 2

3 Improvisational Workflows in Intelligence Automated Modeling DL-based integration of data schema models with process models Feedback on Model Quality Workflow Execution Runtime monitoring and adaptation by glue agents (intelligent connectors) Component Systems & Models Services-based Infrastructure (e.g., NCES) Predictions & Metrics for Correctness, QOS, etc. Interoperability Gaps Workflow Composition Automatic generation of adaptive agents as glue code Interoperability Analysis Model-based Reasoning Techniques TECHNICAL APPROACH Composed System & Composite Model Use Semantic Web technologies for on-line software composition of service-enabled components - Leverage existing web services infrastructure and graphical workflow composition tools - Compatible with DISA NCES core services and architectures Analyze proposed workflow properties by combining semantic service descriptions with simulations and static tools Further develop adaptivity technologies to perform agile execution control System administrators collect, curate, and store successful workflows in a component library MAIN OBJECTIVE Prototype an agile processing pipeline - Similar to the Khoros/VisiQuest rapid prototyping environment for image processing - Allow analysts to dynamically build the software support for custom analytic workflows Support on-line invariant analysis and late-binding execution technologies - Automatically fuse QoS and security properties to generate glue agents and adaptive monitors Leverage the collective expertise of analysts to evolve the most useful and relevant workflows EXPECTED IMPACT Substantial increase in the number of custom analytic processes - Empirical evidence to show that the IT responds to actual analyst tasks, rather than vice versa - Allows IT support for changing analytic processes to evolve without massive reintegration Substantial decrease in the time to produce acceptable software workflows - Faster and more correct, with smaller skill dependence - Increased completeness of processing requirements Orients the IT around analyst requirements - Pull model where users and other IC applications generate the analytic pipelines they need 3

4 Dynamic User s NGA Task SME Cognitive Task Analyst Designer Computable Spec Generator Generator reasons over domain and task semantics to dynamically adapt interface to the situation is automatically tailored for each user Machine learning TECHNICAL APPROACH NGA Analyst adapts to the user s context Use situation theory to quantify the information content of a UI Decompose analytic tasks into UI task specifications - Leverage OWL to create a tractable logic language that can express analytic tasks and data semantics - Apply constraint-based solvers, CBRs, and other planning technologies to yield task-specific UI specs Map UI tasks onto available graphical elements - Build a semantically characterized set of UI graphical elements by using OWL/S and SWRL - Use a planner/shape grammar and machine learning to derive the UI layout for an analyst s individual profile Dynamically create the new UI on the analyst s desk MAIN OBJECTIVE Replace current mass-produced general-purpose UIs with task-sensitive, analyst-specific interfaces Customize each analyst s I/O with the data sources - Analyst interacts with the web at the problem level - Analyst does not have to master all the data sources and algorithms that are available - UI is automatically built for each analyst s unique cognitive/perceptual talents, training, experience, and current problem context Allow UIs to better support independent hypothesis generation and unconventional concept exploration EXPECTED IMPACT Faster and higher-quality analytic output - Embrace individual styles and competencies - Tune core UI planners to allow rapid confirmation or disconfirmation of different uncommon hypotheses - Increase user satisfaction Increased agility in response to new missions - Restructure planners and interfaces on the fly to handle new information requirements and data sources - Interaction with the DB structured around analyst task requirements, not data structures - Late binding the UIs relative to the individual analyst, task, and problem context allows for rapid learning and evolution of interface paradigms 4

5 Last Thoughts Success = creating the conditions for early adopters to allow the semantic web revolution to succeed DAML has had incredible success - We have gone from DARPA-hard challenge to accepted industrial standard in four years - The PM has lost control of the technology It is time to leave the DARPA nest and fly - There is more work to be done: OWL 2.0, Semantic Web Services, Rules, Query Languages, Tools, Documentation, Killer Apps, Proof Exchange, Trust - Domain-specific ontologies and applications - More standards, collaboration with Europe, funding organizations - More nonacademic conferences DAML s intellectual thread will be carried by other programs and organizations 5

6 Thanks Thank to You Jack, Angela, and Arrin Thanks to Jim Hendler and Murray Burke Thanks to all of you who changed the world! 6