March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Context Adaptive Service Framework(A Proposed Intelligent Agent and Knowledge Based

Smartphone Application and Web Service Framework)

John A. Yanosy Jr.jyanosyjr@gmail.com

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Table of Contents

• Limitations of Current Smartphone And Web Services Ecosystem

• Complex Smartphone User Ecosystem

• Conceptual Context Adaptive Framework

• Context Adaptive Desired Characteristics and Enabling Technologies

• Context Knowledge and Adaptation

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Limitations of Current Smartphone And Web Services Ecosystem

• User Cognitive Complexity: Smartphone Applications and Web Services Increase Opportunities while Increasing User Cognitive Complexity

• Static Smartphone Framework: Current Smartphone application environments are static in nature and do not enable real time dynamic selection of applications and services to suit the specific user situational context. Only applications previously loaded are available for real time dynamic use.

• Complex Application Discovery: User’s ability to successfully discover and select smartphone applications that are most relevant to their desires is becoming more complex due to the rapidly increasing numbers and types of applications offered

• No Adaptation to User Context: The only context adaptation is for location based services for discovery on maps using current location. There is no other user context information used to adapt services.

• Application Binding to Specific Web Service/Content Provider: Smartphone applications are typically bound to one or a very small set of Web Services and Providers.

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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* Life Assistance Services

* Multi-AgentSystem

*ServiceCoordination

*SemanticDialog

*OntologyTranslation

Complex Smartphone User Ecosystem(Smartphone Applications and Web Services Increase

Opportunities while Increasing User Cognitive Complexity)

Citizen

Family

LearnerEconomicPlayer

UserRoles

User Desires

Entertainment

Art, Culture,Beauty

Health

Knowledge

Basic(Food, Shelter)

Financial

Companionship

Spiritual

Assistance,Guidance

Life Accomplishment

Provider

Educator

Leader

Contributor

Freedom, Democracy

Society

Semantic WebServices & Domain

Ontologies

CompetitorNurturer,

HealerInnovator

Social

On

tologies

Communication(e-mail, Telephone, Mail)

Information & ProcessingRadio, TV, Cable,

Newspapers, Magazines)

Entertainment(Movies, Music, Games)

Art, Culture(Plays, Musicals, Museums,

Books)

Environment(Monitoring, Control)

PersonalAssistant

Security

E-Commerce, M-Commerce(Monitoring, Control

Consumer

Professional

Citizen

Learner

Artist

User Context User Dialog System Dialog

Commerce

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Context Adaptive Desired Characteristics and Enabling Technologies

• Able to achieve user perceived adaptive behaviors through use of collaborative intelligent agent framework and knowledge of current and past context adaptations

• Represent and reason about user preferences, situational contexts, past decisions and their outcome

• Represent and reason about knowledge of services in a continuously expanding services ecosystem

• Expanding set of Specialized Collaborative Network of Intelligent agents that learn about user situational context for service selection action decisions, their outcomes, and adjust their knowledge and beliefs to guide future decisions in similar situations

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Context Knowledge and Adaptation• Situational Context and Different Perspectives: different people viewing the same situation can

have different perspectives about what is important and which attributes to focus on. • Context Knowledge Representation and Reasoning: How can context knowledge be represented

in a computer system and how is machine reasoning enabled?• Context Structure: Is there a structure or hierarchical context for different Smartphone user

situations, where a particular context may be part of a larger context? Can you list different general context situations?

• Separate of Knowledge and Decisions: what are the characteristics of an overarching framework that utilizes expanding knowledge of user situational contexts and available services while taking decision action or task decisions by an intelligent reasoning machine agent.

• Knowledge Based Reasoning Logic and Intelligent Agents: What is the relationship between reasoning using knowledge inference using ontologies and the kind of reasoning an intelligent would make for decisions?

• User Context Dynamics: Is it beneficial for the user to provide the system clues as to the current situational context? Is it desirable for the user to be able to have multiple contexts simultaneously? How should the system use the context clues from the user, the clues form the environment, from the personal profiles, from historical decisions?

• System Adaptation: Is it useful think of the scope of system adaptation to user context, primarily oriented to service adaptation? What other system adaptations are related to user context? Should these desired adaptations be part of the user context knowledge model?

• Smartphone Users: Do Smartphone users present unique context situations that do not occur in fixed location environments? What are some of these unique context situations? Add them to the previous list and context taxonomy for users.

• Conflicts: When user preference are stored in a system, and user context clue given at system interaction time, how should the system resolve the conflicts between user stored preferences and learned decisions. Should the user be able to set context priorities, or should the system always give preference to instantaneous clues and override preferences. What if one or more people are involved in communications or other shared activity across the system, are there opportunities for translation adaptation when their different context perceptions and preferences are in conflict?

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Key Elements of Context Adaptive Framework

• Context Adaptive Service Framework that represents and reasons about user context knowledge

• Knowledge– User Situational Context Knowledge– Knowledge of Available Services

• Distributed Intelligence – Intelligent Agent Based Service Framework

• Creation of a research map identifying all related research areas

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Conceptual Context Adaptive Framework Model

Advanced Service Vision Conceptual Model

UserDesires

ServiceCoordinator

Context ServiceDiscovery

Ontologies

TranslationSelection

Agreements

ServiceExecution

DeviceCapability Meta Service

Descriptions

UserPreferences

Reusable SemanticWeb TypeServices

Devices

DeviceConstraints

ServiceOntology

Relationships Multi AgentSystems Research

High user burdenfor discovering,accessing andusing services

User-NetworkSemantic DialogSeverely Limited

Next generation WWW Model

representing services & information in semantic context

Adapts, selects services to satisfy

user preferences, user context, trust models, device constraints, &

semantics of user desires

User CenteredDesign Research

Semantic Web Sites Private IntranetsDomain

Ontologies

ConvergedServicesResearch

Context AwareResearch

Semantica, OntologyResearch

Multi Agent Systems

Semantica Knowledge

Sharing, Dynamic

Teams, Open Services Research

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Conceptual Model Characteristics• Distributed intelligence based on knowledge

representation and intelligent agents (W3C Semantic Web and FIPA Agents)

• Ontologies for representing knowledge about context and services

• Intelligent agent model providing service mediation functions– Device adaptation– User Dialog translation– Service coordination– Service selection

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Intelligent System Model

Intelligent System Model

Reasoning Logic

Domain Ontology

World Model

Input Processing

Evaluation

Environment

Action Plans

Action Execution

Goals

Update WorldKnowledge Model

AnalyzePerceptions

Agent Speech Acts,System Output

Coordinate Actions

Selects Planto ExecuteKnowledge Base

Capability SupportsWorld Model Updates,

& Evaluation

System Goals, Policies

Determine set ofplans to achieve goals

Environment definition depends on overall system context. Most likely multiple intelligent subsystems

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Intelligent System Model Characteristics

• Intelligent Agent Architecture with:– World environment model,– Agent communications language,– Ontology commitments,– Goal oriented planning– Intentional task execution – Domain ontologies

• Feedback structure with agent deliberation of environment knowledge, goals, and task planning, and current task viability

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Research Map ConceptAdvanced Service Vision

Business Models-Scenarios

User Centric Service Environment

Adaptation

Policy Framework

User Desires

Context Aware Adaptation

Resource Allocation

Advanced Service Architecture

User Intentions/System Services Translation

User Preferences - Ontology

Nat Lang I/Fs

Distributed Artificial Intelligence

Evolutionary Adaptation

Machine Learning

Service Framework

Web Service & Information Semantic Representation

Service Mediation

Service Discovery

Service Coordination

Service Composition

Ontology Learning

Service Reuse

Automatic Service Execution

ServicePolicies

March 2012 Copyright (c) John A. Yanosy Jr. All rights reserved.

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Candidate Major Research Topics• Service Framework Vision

• User Centric Environment

– User Desires

• Adaptation- Desired Characteristics

– Policy Framework

– User Context Adaptation

– User Context Representation

• User Interface

• Common Service Framework Functions

• Service Representation, Discovery, Composition, Execution

• Semantic Web

• Intelligent Agents