1. Active Active, a platform for building intelligent software
Dr. Charles Baur (EPFL)
Adam Cheyer (SRI International)
Didier Guzzoni (EPFL)
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2. Presentation Plan Introduction Active Framework Applications
Problem space
Active proposition
Active Framework
Active Ontologies
Implementation
Methodologies
Applications
Conclusion
Introduction
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3. Motivation Our information environment is rich and complex
Ubiquitous access to a wealth of data and services
Software and hardware industry constant innovations
UIs have not changed: Simple click-and-do approach not enough
Need for computer assistants
Interact naturally with humans
Can be delegated complex tasks
Observe, understand, anticipate and act
Introduction
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4. Intelligent Systems Resolve Anticipate Interpret Plan action
Understand
Resolve
Anticipate
Interpret
Plan action
Observe
Act
Effectuate
Communicate
Listen
Vision
Sense
Intelligent Systems : Naturally collaborates with human users to deliver services and contents through adaptable, efficient, multimodal user interfaces.
Introduction
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5. Difficult Task Requires numerous AI techniques
Language processing
Plan execution
Dynamic service brokering (MAS)
Implementation is difficult
HCI components (speech recognition, vision, robotics)
Large teams of specialists
Different programming languages and platforms
Testing, debugging and maintenance is difficult
Performance is likely to be affected
Lack of an integrated tool and methodology to easily and effectively build intelligent systems
Introduction
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6. Goal of Active
Provide programmers with an integrated framework and a methodology to build complex AI-based systems
Capable of encapsulating AI techniques
Language processing, plan execution and agent type techniques
Programmer friendly
Small teams
Based on popular programming languages (Java/Javascript)
Offers an IDE (code, test, debug and deploy)
Open and standard compliant
SOA-based (SOAP, REST, RMI)
Deployment (Java, J2EE)
Active Framework
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7. Basic Concept : Active Ontologies
Ontology : A data structure
Formal representation for domain knowledge
Classes, attributes, relations P
movie P
genre P
actor P
rating
Active Ontology : A processing environment
Processing elements arranged according to ontology notions
Communication channels
Active Framework
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8. Active Ontology : Processing
Production Rule System
Rules Sets
Rules (Conditions, actions)
Data store (facts)
Current state of the system
Evaluation Engine
Evaluation passes
Active Innovations
Organizing rules around Ontologies helps design and debug
Developer friendly rule language. Enhanced Java/Javascript with unification P
movie
rule set
rule
condition
action
Active Framework
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9. Active Application Design
One or more Active Ontologies
Hosted on Active runtime
Typically :
Language processing
Plan execution
Dynamic service brokering
Service oriented (SOA)
Loosely coupled
Sensors (user interfaces, speech recognition, vision)
Actuators (robots, user interfaces)
Reusable
Dynamically swapped
Active runtime
Optionally hide. Spend less time on it.
services
Active Framework
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10. Implementation Active Server Active Editor Active Console
Hosts Active Ontologies
Maintains a fact store
Runs evaluation engine
Extensions
Active Editor
IDE
Code, deploy, test
Pluggins
Active Console
Manages Active Server
Active Editor
Active Ontology
Active Server
deploy
Facts store
debug
Evaluation
Engine
monitor
Active Ontology
Active Console
Active Ontology
Active Ontology
Implementation
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11. Active Server Runs and hosts Active Ontologies Implementation
Evaluation Engine
Fact Store
Implementation
Java application/J2EE webapp
SOAP / RMI interface
Rule language is Java/JavaScript enhanced by unification
Extensions
Encapsulate pre-compiled complex operations
Active Server
Evaluation
Engine
Active Ontologies
Fact store
Extensions
Connect out
SOAP/RMI interface
services
Implementation
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12. Active Editor IDE Active Server Connection Plugins
Graphical editing of ontologies
Specialized concept and rule editors
Active ontology definition files saved locally (XML)
Active Server Connection
Deploy/undeploy edited ontologies
Integrated test/debug
Plugins
Automatically creates concepts and rules based on interactive wizards
Show relationships names options.
Implementation
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13. Active Console Management tool Query (read) panel Store (write) panel
Monitor and configure deployed Active Ontologies
SOAP/RMI interface
Query (read) panel
Construct complex queries to Active Server
Tabular result sets
Store (write) panel
Stimulates Active Ontologies by sending events to the server
Could be hidden
Implementation
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14. Active Methodologies Language Processing Agent techniques
Chart Parsing
Event based
Agent techniques
Delegated computing
Dynamic service selection
Plan execution
Process Execution Engine
Reactive Planning
Methodologies
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15. Language Processing : Grammar-Based
Chart parsing
Advantages
Formal parsing (Mathematical expressions)
Deterministic
Disadvantages
Not flexible
Not robust to missing words
Not well suited for non-reliable input modalities (Speech recognition)
Add: Screen Shot from chart parser
Methodologies
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16. Language Processing : Domain-Based
Implementation
Bottom Up
Leaves : Word set, regex
Nodes : Gather, Select
Context
Kept among utterances
Errors, Suggestions
Advantages
Robust to syntax
Ports well to different languages
Wizards
Easy modifications
“find action movies in San Francisco”
“nearby Chinese restaurants”
Popup a leaf wizard in the animation. Second utterance. Shared address -> Context. Half-life: stay in context get gradually weaker. Change probability to confidence. Final command should not stay red.
Methodologies
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17. Activity/Dialog Modeling
Dialogs, Activities, Behaviors
Full-featured workflow management
State / Transitions
Flow instances, Instance space
Basic flow constructs
Start, End
Wait, Switch (branching)
Parallel, sequences
Active Implementation
Set of plugins (Editor Wizards)
Extension (Java/JavaScript) to access flow variables
Has the core primitives such as BPEL. Popup Wizard Just do by drag and drop. Front end : language processing. Second tier: Use the term business logic term. Backend : dynamic services delegation. Then you can build your entire application in Active.
Methodologies
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18. Dynamic Service Brokering
Delegated Computing
What instead of how or who
Service Registry
Service categories cross-ontology references
Service instances (providers)
Broker
Parallel, Sequential, Broadcast
Third-party meta-agents
Active Implementation
Specialized Active Ontology
Server Extension, IDE Wizards
Enable Fandango. One more Wizard : Test panel.
Methodologies
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19. Prototypes / Demonstration
Information Retrieval Assistant
Meeting Organizer Assistant
Operating Room Assistant
Prototypes
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20. Information Retrieval Assistant
Language Processing
Plan Execution
Delegation
Active Server
SOAP
Extensions
gmail SMTP/POP server
Google Movies
Yahoo! Local
City Information
Opentable
Prototypes
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21. Meeting Organizer Assistant
Active Server
Language Processing
Plan Execution
Delegation
Extensions IM
SOAP
gmail calendar
gmail SMTP/POP server
Prototypes
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22. Operating Room Assistant
Mouse 3D
Speech Recognizer
Patient vital signs probes
Context History
Hand/Head
Tracker
Language Processing
Plan Execution
Powered Endoscope
Actions through Delegation
User Interface
Gesture Recognizer
Text to Speech
Prototypes
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23. Active Advantages One platform to learn for programmers
Can build all three tiers of applications in Active
One language and tool to learn
Easier to debug, test and deploy
AI Encapsulation-Lowering the bar
Methodologies encapsulated as Active Wizards and Extensions
NLP, process execution, service brokering
Componentized reusable sensors and actuators
TTS, Speech, Gesture recognizers, vision systems
Open and Standard-based
SOA design
Ease of integration through SOAP and REST
Reuse of existing components in multiple applications
Bringing AI to the masses.
Conclusion
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24. Ongoing Work Research Topics Active implementation and features
Combine activity recognition with process execution
Implement and evaluate BDI-like behaviors with Active (goal and intention stack)
Active implementation and features
Scalability
Performance optimizations
Lightweight embedded Active Server
User Evaluations
Put some of our systems on-line
Measure feedback from surgeons (Intelligent Operating Room)
Conclusion
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25. Thank You !
Questions ?
Suggestions ?
Remarks ?
Conclusion
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