This document discusses the eCeNS Hybrid Knowledge-Base and SCN-Engine Framework for building Intelligent Systems. The framework uses a graph-based hybrid knowledge base, domain-specific ontologies, dynamic taxonomies, and a neuron-inspired symbolic-computational network to sense, perceive, learn from, and interact with environments. The goal is to create a general-purpose framework that allows building various domain-specific intelligent systems using machine learning, knowledge representation, and other AI techniques.

1. 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

2. 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,
2
• 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.

3. Intelligent Systems need
Ambient Intelligence
3
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

4. 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.

5. 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

6. 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

7. eCeNS Approach to General IS Framework
Our approach is to explore and pursue
1. 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.

8. Domain-Specific
Ontologies
to understand Things and
how They relate to each
other
World
Model
to Understand
Real-World: people, places
and things, their
contexts, histories and
behaviors, etc.
Dynamic
Taxonomies
to know how to
Differentiate and to
Recognize Patterns
Probabilistic
Neuron-Inspired
Symbolic –Computational
Network
to Sense, Perceive and
Learn
SCN Engine and
Knowledge-base
Light
Temperature
Location
Time
ETC.
eMail
Messages
RSS
Documents
ETC.
Sensory
Input
Actuator
Output
Light Switchs
Thermostats
Controllable
Devices
Alarms
ETC.
eMails
Messages
RSS
Documents
ETC.
RealWorld
RealWorld
DigitalWorld
DigitalWorld
People
Sound
Speakers
Social
Networks
People
eCeNS HKB and SCN Engine Framework

9. 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
Sensing
Actuating
InputMessages
OutputMessages
Editor (GUI)

10. 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.
10

11. 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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12. 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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Node
Attributes Reln. Attributes
Relationship Label

13. 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.
13
Entity Concept
Property
Value Value
Category
IS_A
IS_IN_*
HAS_VALUEHAS_VALUE
NEWEST_VALUEOLDEST_VALUE
OLDER_VALUE
HAS_PROPERTY
Property History

14. 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.
14
ConceptEntities
Property
HAS_PROPERTY
Category
or
Concept
IS_IN or IS_A
Concepts
Relationship
Labels
Constraints
IS_A
Concept

15. 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_MEMBER
Sub-category Predicate
Category
Concept
HAS_SCHEMA

16. 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.
16
Temperature
Freezing Cold Warm Hot
HAS_MEMBER
Attributes:
UOM = Celsius
type Ordinal
0° 16° 28°

17. SCN’s
Symbolic 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).
17
Entity
Property
HAS_PROPERTY
Entity
Property
HAS_PROPERTY
P+P
Neuron
Other Input
Properties
NOTIFY
NOTIFY
Neuron Function
Neuron Connections:
{ P+P, P+C, P+E, CP, EP }

18. 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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Sensor
Entity
HAS_PROPERTY
Property
Room
Entity
HAS_PROPERTY
Room Temp.
Property
Temperature
Category
Neuro
n
NOTIFY
NOTIFY
Freezing
Category
Cold
Category
Warm
Category
Hot
Category
HAS_MEMBER
IS_IN_CATEGORY
IS_IN_SUBCATEGORY_OF

19. 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
Furnace
Property
{ Freezing, Cold, Warm , Hot }
{ Day, Night }
{ Off, Cooling, Heating }
Cooling
Off
Heating
Night
Day
Hot
˄
˄
˄
˄ Off
Warm
Freezing | Cold

20. Exploring SCN as a Means to
Implement 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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21. 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.
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22. 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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23. Thank You
George.Vanecek@huawei.com
FutureWei Technologies, Co., LTD.
2330 Central Expressway, Bldg. A.
Santa Clara, CA, 95051 USA