ecens hybrid knowledge-base and scn-engine framework for building intelligent systems
DESCRIPTION
For intelligent systems to sense, perceive and learn from their environment and the Internet, they need to create and manage the knowledge needed to perform their required tasks. This talk introduces the eCeNS hybrid knowledge base and perception system being developed at the Innovation Center in Santa Clara, CA. The system is being architected as a general-purpose framework for building domain-specific intelligent systems with emphasis on ambient intelligence and organic computing. The talk will also include a demonstration of the system's visual graphical editor and two POC use cases, one for home automation, and one for enterprise email-based context analysis. The talk will conclude with a more detailed presentation of the system’s model for its world model, ontologies, concepts/taxonomies, and its neuron-inspired symbolic computational network, as well as the interoperability with external modules.TRANSCRIPT
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www.huawei.com
eCeNS Hybrid Knowledge-Base and SCN-Engine Framework for
building Intelligent Systems
Dr. George Vaněček, Jr.Innovation Center, FutureWei Technologies
Santa Clara, CA
February, 2013
Presented at SV CMU, Feb 12, 2013
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Our Focus on Intelligent Systems
• With the pervasive growth of Social Networking, the Web, and the emerging Internet-of-Things, the digital world is becoming more aware of the real-world,
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• partially influenced by the advances in ambient intelligence and its adaption in computerized and Internet-connected devices
• Intelligent systems will continue to gain ambient intelligence to better sense, perceive and learn their environments
• and apply organic computing methods to respond to or to cause changes in their environments.
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Intelligent Systems needAmbient Intelligence
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AmI refers to electronic environments that are sensitive and responsive to the presence of people
“In an Ambient Intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in easy, natural way using information and intelligence that is hidden in the network connecting these devices.”
Source: Wikipedia
Source: Wikipedia
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Intelligent Systems also need Organic Computing
to dynamically adapt to their environments and tasks with abilities that are• Self-Configuring,• Self-Describing/Explaining,• Self-Healing,• Self-Protecting,• Self-Organizing,• Context-aware, and• Reactive and Proactive,
• with minimal human intervention.
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Increasing Intelligence in Systems
“Intelligent Systems will exist in environments they sense
and perceive, and from which they learn and continually act to
achieve their objectives.”
1. sense the real-world environments,
2. perceive the world using world models,
3. adapt to different environments and changes,
4. learn and build knowledge, and
5. act to control their environments.
They are computational systems that behave intelligently and rationally, to
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Machine Learning and AI Requirements
• Build systems that learn about self and environment• Create Situated Autonomous Decision Systems
in dynamic environments over extended time entrusted to handle complex tasks
• Teach autonomous systems how to handle time, change, and event streams. Most systems do not handle time and changes well
• Build Agents that exhibit life-long Machine Learning (ML) rather than ML algorithms that learn one thing only.
• Create an interchangeable world knowledge for Intelligent Systems.
Source: AAAI-96
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eCeNS Approach to General IS Framework
Our approach is to explore and pursue1. a single, general-purpose, hybrid KB framework
based on a data-driven, temporal and probabilistic, graph representation that
2. integrates a dynamic model of the world with its learned ontologies and taxonomies, and
3. a neuron-inspired symbolic-computational-network to drive all perception and learning, with
4. sensing and actuating abilities, and that
5. allows building various domain-specific intelligent systems.
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Domain-SpecificOntologies
to understand Things and how They relate to
each other
WorldModel
to UnderstandReal-World: people,
places and things, their contexts, histories and
behaviors, etc.
DynamicTaxonomies
to know how to Differentiate and to Recognize Patterns
ProbabilisticNeuron-Inspired
Symbolic –Computational Network
to Sense, Perceive and Learn
SCN Engine andKnowledge-base
Light
Temperature
Location
Time
ETC.
Messages
RSS
Documents
ETC.
SensoryInput
ActuatorOutput
Light Switchs
Thermostats
ControllableDevices
Alarms
ETC.
eMails
Messages
RSS
Documents
ETC.
Re
al W
orld
Re
al W
orld
Dig
ital W
orld
Dig
ital W
orld
People
Sound Speakers
SocialNetworks
People
eCeNS HKB and SCN Engine Framework
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eCeNS Key Components
1. A graph-based hybrid knowledge base
2. An eCeNS RESTful Web Service that supports a RESTful API for management and control
3. A RESTful Sensing Service that listens for and consumes external structured messages (in JSON) and infuses them as related entities into the world model. This initially excites neurons that then process and propagate the data through the World Model.
4. A RESTful Actuation Client that receives neural signals from the World Model and marshals the related entities into JSON to be sent to external services.
5. An SCN Engine that sequences and executes excited neurons within the World Model.
HKB
WM DSO
Taxonomy SCN
SCN Engine
RESTful Web Svc
Sens
ing
Actuating
Inpu
t Mes
sage
s
Output M
essages
Editor (GUI)
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Hybrid Knowledge Base
The HKB represents:
1. World model (WM) of attributed entities, properties, and their relationships,
2. Domain-Specific Ontologies (DSO) that generalize the world model in terms of related concepts and their constrained relationships,
3. A set of taxonomies denoting category hierarchies for abstracting the concept properties with associated rules, as used for concept differentiation, and
4. A neuron-inspired Symbolic Computational Network (SCN) that propagates information and knowledge between the world model and the DSOs properties.
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eCeNS KB Editor and Simulation Demos
Simple Home Automation:• In a smart house with a
HVAC and sensors for lights, temperature and door status,
• Keep a room warm• As long as the lights are on
and the door is closed.
Simple Enterprise Email-based Context-Awareness:
• Use NLP to identify subject phrases from eMails
• Build a user-group/topic context-awareness model
• Drive an intelligent UCC mobile application with current context information
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KB Nodes and Links
• The eCeNS HKB is represented as an attributed and labeled directed graph.
• Nodes maintain both out-links and in-links.• Each node or directed link has an associated set of
name/value attributes used for meta-data, such as node types, time stamps, or scoring.
• Nodes represent entities, properties, property values, concepts, categories and neurons, while the links represent attributed relationships between the nodes.
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NodeAttributes Reln. Attributes
Relationship Label
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World Model Entities
• An entity (and its properties) is an instantiation of a concept, where the concept is an entity generalization as defined in an associated ontology.
• Entity is represented by an entity node.
• Entity details are defined by an associated set of zero or more properties represented as property nodes.
• Properties are defined by a given concept (or a generalized category defined in an associated taxonomy).
• In general, properties are named values that may change over time.
• These changes are maintained by the properties histories.
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Entity Concept
Property
Value Value
Category
IS_A
IS_IN_*
HAS_VALUEHAS_VALUE
NEWEST_VALUEOLDEST_VALUEOLDER_VALUE
HAS_PROPERTY
Property History
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DSO’s and their Concepts
• An ontology is a generalization of the World Model.
• It is defined by concepts and their constrained relationships and maps the concept properties to well-defined categories in the associated taxonomies.
• The concept nodes and their constrained relationships need to be either defined manually, or learned from the World Model patterns.
• Once known, ontologies are used to instantiate their conceptual structures within the World Model.
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ConceptEntities
Property
HAS_PROPERTY
Categoryor
Concept
IS_IN or IS_A
Concepts
RelationshipLabels
Constraints
IS_A
Concept
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Taxonomy Categories
• A taxonomy is a hierarchical structure of categories for recognizing members (concepts or entities) of well-defined sets.
• It provides a mechanism for assigning meaning to ordinal and cardinal values and concepts.
• A category can be partitioned into sub-categories.• Each sub-category has a characteristic-function (predicate)
for mapping members of the category into the specific sub-category.
• Taxonomies can be replicated to personalize partitioning.15
Category
HAS_MEMBERSub-category Predicate
Category
ConceptHAS_SCHEMA
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Example Category
• Each HAS_MEMBER link has an associated characteristic function.
• For now, these are closures such as: (t){ return t < 0 }
• Sub-categories form a partition of the category set.
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Temperature
Freezing Cold Warm Hot
HAS_MEMBER
Attributes:UOM = Celsiustype Ordinal
0° 16° 28°
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SCN’sSymbolic Neurons
• As a data-driven system the SCN models all the mechanisms for sensing, perception, learning and acting by symbolic neurons.
• Neuron is a generalized computation flow-control element that is connected to a set of input property nodes and optionally to a single output property node.
• Whenever any of its input properties changes, the neuron executes its function on all its input properties, and possibly generates a change in its output property (or structure).
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Entity
Property
HAS_PROPERTY
Entity
Property
HAS_PROPERTY
P+PNeuron
Other InputProperties
NOTIFY
NOTIFY
Neuron Function
Neuron Connections:
{ P+P, P+C, P+E, CP, EP }
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SCN Example Model of Categorization
• “Category” neurons map category properties into sub-categories
• Taxonomy categories with their characteristic functions are used to determine memberships.
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SensorEntity
HAS_PROPERTY
Property
RoomEntity
HAS_PROPERTY
Room Temp.Property
TemperatureCategory
Neuron
NOTIFYNOTIFY
FreezingCategory
ColdCategory
WarmCategory
HotCategory
HAS_MEMBER
IS_IN_CATEGORY
IS_IN_SUBCATEGORY_OF
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SCN Example Model of a Logic Program
• Logic Programs automatically generated with Rule neurons link a given set of input properties to the output property.
• Specific interpretation is driven indirectly by the constraints in the related categories (e.g., Temperature, Time-of-Day, HVAC-Status).
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Temp.Property
T.o.D.Property
FurnaceProperty
{ Freezing, Cold, Warm , Hot }
{ Day, Night }
{ Off, Cooling, Heating }
Cooling
Off
Heating
Night
Day
Hot˄
˄
˄
˄ Off
WarmFreezing | Cold
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Exploring SCN as a Means toImplement Supporting AI Methods
As a Graph-based Symbolic Computation Network, SCN can be used to model• Concept and Category Learning• Logic Programming and Various types of Reasoning
Analogical, specialization, generalization, meta-level, etc.
• Probabilistic Reasoning and Bayesian Inference• Hybrid Artificial Neural Networks (ANN)• Natural Language Processing (NLP)• Stochastic Computing • Workflow Processing• Genetic Algorithms• Others?
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The following nine groups of colors are an example of how our design colors can be used, please take note that you should only use one design color group per slide. For specific usage details, refer to the “Typesetting Standard”.
Key eCeNS Mechanisms being Developed
• Automatic sensing: Process and categorize new schemas of incoming JSON messages (via REST)
• Automatic actuation: Automatically create entities for generating outgoing JSON messages (via REST)
• DSO Generalization and Category Learning through Probabilistic Reasoning: Learn DSO’s by recognizing WM patterns as related concepts; and categories as a way to differentiate concepts
• WM Specialization: Create entity structures by ontology cloning.• Explore Neural Models: Identify necessary sets of neural functions
and their models to support needed types of reasoning and machine learning algorithms.
• SCN Engine Processing: Explore SCN Engine neuron scheduling• SCN Creation: Explore Ontology SCN to WM cloning and refinement• External Module Interoperability: Explore interoperability with external
Machine Learning and Data Processing tools and modules.21
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The following nine groups of colors are an example of how our design colors can be used, please take note that you should only use one design color group per slide. For specific usage details, refer to the “Typesetting Standard”.
Summary
• Current trends in academia and industry focusing on Big Data and Machine Learning (as a way to address context-awareness, ambient intelligence etc.) are driving much excitement in new ways of solving real-world problems.
• Classical AI methods and Knowledge Bases create the foundations.
• Black-box solutions are great but are not general and become obsoleted quickly.
• New general-solutions, frameworks and platforms are essential to deal with long-term Intelligent Systems realizations.
• eCeNS is rooted in classic AI methods but driven by innovation in new and emerging methods.
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The following nine groups of colors are an example of how our design colors can be used, please take note that you should only use one design color group per slide. For specific usage details, refer to the “Typesetting Standard”.
Thank You
[email protected] Technologies, Co., LTD.2330 Central Expressway, Bldg. A.
Santa Clara, CA, 95051 USA