Context Adaptive Services

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An overview of some concepts for enabling a more intelligent Smartphone application eco system for both users and application developers.

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Context Adaptive Services

  1. 1. Context Adaptive Service Framework(A Proposed Intelligent Agent and Knowledge Based Smartphone Application and Web Service Framework) John A. Yanosy Jr. jyanosyjr@gmail.comMarch 2012 Copyright (c) John A. Yanosy Jr. All 1 rights reserved.
  2. 2. 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 AdaptationMarch 2012 Copyright (c) John A. Yanosy Jr. All 2 rights reserved.
  3. 3. 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 3 rights reserved.
  4. 4. Complex Smartphone User Ecosystem (Smartphone Applications and Web Services Increase Opportunities while Increasing User Cognitive Complexity) Semantic Web Services & Domain Ontologies Personal Assistant Communication User User Desires (e-mail, Telephone, Mail) * Life Roles Freedom, Democracy Art, Culture, Assistance Information & Processing Professional Beauty Services Radio, TV, Cable, Leader Entertainment * Multi-Agent Newspapers, Magazines) SpiritualContributor Citizen Social System Entertainment Companionship *Service (Movies, Music, Games)Educator Consumer Family Society Coordination Assistance, Art, Culture Learner Guidance *Semantic (Plays, Musicals, Museums, Provider Life Dialog Books)Economic Learner Player Knowledge Accomplishment *Ontology Citizen Environment Translation Competitor Financial (Monitoring, Control) Nurturer, Basic Healer Health (Food, Shelter) E-Commerce, M-Commerce Innovator (Monitoring, Control Artist Security Commerce User Context User Dialog System Dialog March 2012 Copyright (c) John A. Yanosy Jr. All 4 rights reserved.
  5. 5. 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 situationsMarch 2012 Copyright (c) John A. Yanosy Jr. All 5 rights reserved.
  6. 6. 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 6 rights reserved.
  7. 7. 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 areasMarch 2012 Copyright (c) John A. Yanosy Jr. All 7 rights reserved.
  8. 8. Conceptual Context Adaptive Framework Model Advanced Service Vision Conceptual Model Next generation Reusable High user burden Adapts, selects WWW Model Semantic for discovering, services to satisfy representing services Web Type accessing and user preferences, user & information in Services using services Service context, trust models, semantic context Ontology User-Network device constraints, & Semantic Dialog semantics of user Relationships Multi Agent Severely Limited desires Systems Research Semantica Knowledge Service Sharing, Execution Dynamic Teams, Open Devices Services Research Device Translation Service User Constraints Selection Coordinator Desires Agreements Context Service Semantica, Ontology Research Discovery Context Aware User Centered Converged Research Design Research Services Ontologies Research User Multi Agent Systems Preferences Device Capability Meta Service Domain Semantic Web Sites Descriptions Ontologies Private IntranetsMarch 2012 Copyright (c) John A. Yanosy Jr. All 8 rights reserved.
  9. 9. 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 selectionMarch 2012 Copyright (c) John A. Yanosy Jr. All 9 rights reserved.
  10. 10. Intelligent System Model Intelligent System Model System Goals, Policies Capability Supports Goals Determine set of World Model Updates, plans to achieve goals & Evaluation Reasoning Logic Evaluation Selects Plan Knowledge Base to Execute World Model Action Plans Update World Knowledge Model Domain Ontology Coordinate Actions Input Processing Action Execution Analyze Agent Speech Acts, Perceptions System Output Environment Environment definition depends on overall system context. Most likely multiple intelligent subsystemsMarch 2012 Copyright (c) John A. Yanosy Jr. All 10 rights reserved.
  11. 11. 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 viabilityMarch 2012 Copyright (c) John A. Yanosy Jr. All 11 rights reserved.
  12. 12. Research Map Concept Advanced Service Vision Business Models-Scenarios Machine Learning User Centric Service Environment Evolutionary AdaptationContext Aware Adaptation Adaptati on User Preferences - Ontology User Desires Policy Framework Nat Lang I/Fs Resource Allocation User Intentions/System Services Translation Advanced Service Architecture Service Mediation Distributed Artificial Service Framework Intelligence Service Discovery Service Web Service & Information ServicePolicies Coordination Semantic Representation Automatic Service Execution Service Composition Ontology Learning Service Reuse March 2012 Copyright (c) John A. Yanosy Jr. All 12 rights reserved.
  13. 13. 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 AgentsMarch 2012 Copyright (c) John A. Yanosy Jr. All 13 rights reserved.

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