Table of ContentsVirtual Sales Agents for Electronic Commerce Outline Intelligent Agent Technology is a Prerequisite for Advanced WebCommerce Five Generations of Internet Applications What are Virtual Sales Agents? Intelligent Web Services XML-based Negotiation between Shopping and Sales Agents Virtual Market Places with Human and Machine Agents Performance Ranking of Comparison Shopping Agents Three Generations of Web Sites The Idea of Personalized Netbots What is a Virtual Web Page? Virtual Webpage Retrieved from 5 Different Servers AiA: Information Integration for Virtual Webpages The Generation of Virtual Webpages with PAN and AiA Persona as a Personal Travel Consultant The Combination of Retrieved and Generated Media Objects for Virtual Webpages The Combination of Retrieved and Generated Media Objects for Virtual Webpages Operational Models of Referential Semantics for Robots and Netbots (Wahlster 1999) The Role of Ontological Annotations for the Generation and Analysis of Virtual Webpages (Wahlster 1999) A Natural Language Agent for Finding Pre-Owned Porsche Cars Towards Mobile and Speech-based E-Commerce Using UMTS Phones Enhanced ECommerce through Personalization Personified Agents Increase the User's Trust in the System's Presentation Impact of the modality of a Presentation on the User's Trustfulness PPP’s Persona Server implements a generic Presentation Agent that can be easily adapted to various applications Use of a Life-like Character for Electronic Commerce DFKI‘s PET-Technology: Flexible Realization of Virtual Sales Agents Classification of Persona Gestures Context-Sensitive Decomposition of Persona Actions Extensions of the Representation Formalism PET: Persona-Enabling Toolkit The Persona Markup Language Functional View of PET The Bidirectional Control Flow on Persona-Enabled Webpages Sending Interface Agents to Clients: Plug-Ins or Applets? Porsche 911 & Boxter Persona Active Elements (PAE) A Virtual Sales Agent for OTTO – World’s Largest Tele-Ordering Company DFKI’s Ecommerce Presentation Planner has been extended to accommodate for various target platforms through the introduction of a mark-up language layer Simulated Dialogues as a Novel Presentation Technique Presentation Teams for Advanced ECommerce Underlying Knowledge Base Example of a Dialogue Strategy Multiple Interface Agents for User-adaptive Decision Support MAUT (Multi-Attribute Utility Theory) - I MAUT (Multi-Attribute Utility Theory) - II MAUT - Example Research Topics: Multiple Interface Agents Non-Interactive Presentation Teams Characteristics of the Interactive Presentation Scenario I Characteristics of the Interactive Presentation Scenario II System Architecture for Miau Multi-Party Dialogue Scenario Multi-Agent Dialogue Control Layer Model for Multi-Party Conversation From Script-Based Approaches to Interactive Performances Evaluation of Presentation Teams Formulation of Hypotheses Settings for the Scheduled Experiment Dialogue Examples for the Experiment Personalized Sales Dialogues with Presentation Teams in the Miau System Information Extraction Agents The Trainable Information Agents Framework (Bauer, Dengler) Overall Architecture Ontological Reasoning for Decision Support: Topic Maps Domain Theory Ontological Reasoning for Decision Support: Topic Maps - I Ontological Reasoning for Decision Support: Topic Maps - II Ontological Reasoning with Topic Maps Example - I Ontological Reasoning with Topic Maps Example - II Ontological Reasoning for Decision Support: Topic Maps - III Programming by Demonstration - I Programming by Demonstration - II Programming by Demonstration - III PbD-based Wrapper Construction for Information Agents New approach to wrapper construction Sample Wrapper Generation using PbD Apply the same wrapper to a new source Apply the same wrapper to a new source (2nd case) PAN-Video High Degree of Parallelism of Queries Knowledge about a Webpage Shared by User and Agent Example - Ontology Query Planning Query Planning Query Plan Visualization Three Levels of Mark-up Languages for the Web M3L Integrates Three Language Families RuleML: Ontology Extensions for Rule Knowledge The Rule Markup Initiative From traditional XML Representation to RDF-like Representation of RuleML Rules Recommender Rule: Forward Markups Recommender Rule: Backward Markup An Example coded in RuleML 08 Two-Way Relationship Between RuleML and RDF Research on Personalized Interface Agents brings disparate subfields in the area of intelligent systems together Conclusion |
Autor: Wolfgang Wahlster & Renato Orsini
E-Mail: wahlster@dfki.de Homepage: http://www.dfki.de/~wahlster |