German Research Center for Artificial Intelligence DFKI GmbH Stuhlsatzenhausweg Saarbruecken, Germany phone: ( ) /4162 fax: ( ) WWW: Monday, 2 July Wolfgang Wahlster Virtual Sales Agents for Electronic Commerce

© W. Wahlster 1.Virtual Sales and Shopping Agents 2.Virtual Webpages 3.Life-like Characters as Virtual Sales Agents 4.Virtual Sales Teams 5.Information Extraction Agents for E-Commerce 6.Wrapper Induction and Programming by Example 7.Ontological Annotation of Webpages 8.Encoding Rule Knowledge for E-Commerce Agents 9.Conclusions Outline

© W. Wahlster Advanced WebCommerce Virtual Web Pages One-to-One Marketing Intuitive, Multilingual Access Dialogue with Virtual Sales Agents Shopbots for Automated Comparison Shopping Text Analysis and Generation User Modeling and Language Generation Coordinated Text & Graphics Planning Robust Dialogue Understanding Advanced Speech Synthesis Information Extraction from HTML/XML Documents Machine Translation Multimodal Interfaces Multimedia Presentation Planning Intelligent Agent Technology is a Prerequisite for Advanced WebCommerce

© W. Wahlster 1 Research Net WWW Mobile Internet Services Embedded Internet Agents Five Generations of Internet Applications Internet Access via WAP and UMTS devices 2000 t Every Car has a homepage, Agents are main Internet users, Ubiquitious Computing

© W. Wahlster ACTIVE appear as life-like characters plan interactive behavior autonomously can initiate interaction What are Virtual Sales Agents? INTER- ACTIVE understand the user‘s requests answer clarification questions allow mixed initiative dialogs INTERNET AGENTS respond immediately to interruptions criticism and clarification questions direct manipulation RE- ACTIVE anticipate the user's needs adopt the user's goals provide unsolicited comments PROACTIVE

© W. Wahlster Consumer Provider sells Information Goods Services buys Information Goods Services Web Sites Knowledge about: Usage Patterns User Models Consumer Profiles Netbot Intelligent Parallel Retrieval Information Extraction and Summarization Personalized Presentation Matchmaking Teleshopping Assistance Telemarketing Assistance Translation Services Data Mining Services Intelligent Web Services

© W. Wahlster XML-based Negotiation between Shopping and Sales Agents Customer Shopping Agent Sales Agents Negotiation based on the Exchange of XML Documents - Call for Bids - Offer - Criticism - Alternate Offer Companies

© W. Wahlster Virtual Market Places with Human and Machine Agents

© W. Wahlster Jango Performance Ranking of Comparison Shopping Agents Performance Ranking Agent Ranked List of all Shopping Agents for a Product Category Benchmark Problem PricescanBuybuddyCadabraMySimonRoboshopper Comparison Shopping Agents

© W. Wahlster First GenerationSecond GenerationThird Generation Static Web Sites Fossils cast in HTML Interactive Web Sites JavaScripts and Applets Database Access and Template-based Generation Dynamic Web Sites Virtual Webpages Netbots, Information Extraction, Presentation Planners Adaptive Web Sites User Modeling, Machine Learning, Online Layout Three Generations of Web Sites

© W. Wahlster Softbots Indices, Directories, Search Engines WWW Traveller’s Netbot: Tries to achieve traveller’s goals (finding and executing plans) checks availability finds best price uses personal preferences (e.g. frequent flyer programme, seating preferences) lets the traveller know, when seats become available (active help) Mass Services Personal Assistants e.g. MetaCrawler The Idea of Personalized Netbots

© W. Wahlster A Virtual Web Page is generated on the fly as a combination of various media objects from multiple web sites or as a transformation of a real web page. looks like a real web page, but is not persistently stored. integrates generated and retrieved material in a coordinated way. can be tailored to a particular user profile and adapted to a particular interaction context. has an underlying representation of the presentation context so that an Interface Agent can comment, point to and explain its components. Virtual Memory, Virtual Relation, Virtual Reality... What is a Virtual Web Page?

© W. Wahlster Virtual Webpage Retrieved from 5 Different Servers

© W. Wahlster AiA: Information Integration for Virtual Webpages Yahoo Weather Server PAN Travel Agent Andi Car Route Planner Yahoo News Server Gault Millau Restaurant Guide Hotel Guide

© W. Wahlster Trip Data The Generation of Virtual Webpages with PAN and AiA Netbot PAN Pictures and Graphics Pieces of Text Coordinates for Pointing Gestures Input for Speech Synthesis Icons for Hyperlinks Hotel Agent Address Weather Agent Train & Flight Scheduling Agent Major Event Agent Virtual Web Presentation AiA Constraint- based Online Layout Presentation Planner Persona Server Components of virtual Webpages Map Agent

© W. Wahlster Persona as a Personal Travel Consultant

© W. Wahlster Information Structures Relations, Lists KR Terms Multi-Domain Problem Specs NETBOT Retrieved Results Media Objects Texts, Sounds, Videos Pictures, Maps, Animations Distributed Information Multiple Data Sources The Combination of Retrieved and Generated Media Objects for Virtual Webpages

© W. Wahlster Select & Design Select Canned Media Objects Design New Media Objects Graphics, Animation Text, Speech, Mimic Icons, Clip Art Frames, Sounds Reuse & Transform Coordinate Media Objects Transform Media Objects Temporal Synchroni- zation Spatial Layout Clip, Convert, Abstract Zoom, Pan, Transition Effects The Combination of Retrieved and Generated Media Objects for Virtual Webpages Information Structures Relations, Lists KR Terms Retrieved Results Media Objects Texts, Sounds, Videos Pictures, Maps, Animations

© W. Wahlster RobotNetbot “Screw” Physical Objects Screw 1Screw N... Set of Recognizers Set of Subsumption Relations in an Ontology “Departure Time” Set of Subsumption Relations in an Ontology WWW Objects DT 1DT N Set of Wrappers... Operational Models of Referential Semantics for Robots and Netbots (Wahlster 1999)

© W. Wahlster Information Extraction Agent Presentation Planner Webpages with Ontological Annotations Webpages without Ontological Annotations Virtual Webpage Presentation Agent Persona Information Extraction Agents TriAS With Ontological Annotations in: SHOE, OML, XOL,OIL, DAML and Persona Annotation inPML The Role of Ontological Annotations for the Generation and Analysis of Virtual Webpages (Wahlster 1999)

© W. Wahlster Boxter, not red, must have AC, less than 20k A Natural Language Agent for Finding Pre-Owned Porsche Cars

© W. Wahlster Towards Mobile and Speech-based E-Commerce Using UMTS Phones UMTS phones (Wireless Application Protocol for Cellular Phones) WML as a markup language for interactive content Mobile access to virtual shops allows price comparisons during real shopping Multimodal dialog: Voice In (Speech) - Web Out (Graphics, Hypertext) Voice input using advanced speech understanding technology Easy to use: customers simply say what they want

© W. Wahlster System is able to flexibly tailor product presentations to the individual user and the current situation. Enhanced ECommerce through Personalization An animated character serves as “Alter Ego” of the presentation system. Personalized Presenters at DFKI

© W. Wahlster Personified Agents Increase the User's Trust in the System's Presentation Experimental evidence for effects of modality on the user's trust (van Mulken, 1999) The system gives recommendations, which turn out to be wrong in some cases. How much does a user trust the system's advice depending on the modality of a presentation? Self-animated Persona, Speech, Gesture, Facial Expression, Pointing Speech, Graphical Highlighting Text, Graphical Highlighting

© W. Wahlster Result: Persona > Speech > Text Conclusion: If the presentation is more human-like, recommendations are more readily followed For decision support systems tutoring systems recommendation systems virtual sales agents personified interface agents have a clear advantage: They increase the user's trust in the information presented by the system Impact of the modality of a Presentation on the User's Trustfulness

© W. Wahlster PPP’s Persona Server implements a generic Presentation Agent that can be easily adapted to various applications Behaviors Presentation Gestures Reactive Behaviors Idle-time actions Navigation actions Auditory Characteristics Sound effects, auditory icons Voice: male, female Visual Appearances Hand-drawn Cartoon Bitmaps Generated Bitmaps from 3D-Models Video Bitmaps Persona Server

© W. Wahlster Use of a Life-like Character for Electronic Commerce Digital Assistant Selector

© W. Wahlster DFKI‘s PET-Technology: Flexible Realization of Virtual Sales Agents Sales Agent on Webpage advanced presentation behavior complex implementation Agent in its own Frame simple implementation limited presentation behavior Sales Agent on Desktop very advanced presentation behavior download of sales agent

© W. Wahlster Classification of Persona Gestures Talking Posture 1 cautious, hesitant appeal for compliance avoids body-gestures Talking Posture 2 active, attentive self-confident uses body-gestures Gesture Catalogue

© W. Wahlster Context-Sensitive Decomposition of Persona Actions take-position (t 1 t 2 ) point-to (t 3 t 4 ) move-to (t 1 t 2 ) r-stick-pointing (t 3 t 4 ) High-Level Persona Actions Context-Sensitive Expansion (including Navigation Actions) Decomposition into Uninterruptable Basic Postures r-turn (t 1 t 21 ) r-step (t 21 t 22 ) f-turn (t 22 t 2 ) r-hand-lift (t 3 t 31 ) r-stick-expose (t 31 t 4 ) Bitmaps...

© W. Wahlster Production Act Presentation Act Introduce Create- Graphics S-Show S-Wait S-PositionElaborate-Parts S-Create- Window S-Depict Label S-PointS-Speak S-Point Qualitative constraints:Create-Graphics meets S-Show,... Metric constraints:1 <= Duration S-Wait <= 1,... Distinction between production and presentation acts (i.e. Persona- or display acts) Explicit representation of qualitative and quantitative constraints Extensions of the Representation Formalism

© W. Wahlster Objective: Enable non-professional computer users to populate their web pages with lifelike characters PET comes with: a set of characters and basic gestures an easy-to-learn Persona markup language Developer’s PET will include: a character design tool which enables users to build their own characters Technical Realization: Based on XML and Java PET: Persona-Enabling Toolkit

© W. Wahlster Specification of the character to be used Specification of Persona actions Persona Test Features: –XML-based –easy to learn The Persona Markup Language

© W. Wahlster Functional View of PET URL of Webpage with Persona Tag Persona Engine Behavior Monitor Character Composer Event Handler Persona Test Persona Scripts waitscreen 4 gesture greet 0 0 null gesture laugh 0 0 null... Audio Data Bitmaps PET Application Server PET Parser PET Generator Webpage with Reference to Java Applet......

© W. Wahlster Text Input Speech Input Menu Input Direct Manipulation Input Mouse Clicks Mouse Movements The Bidirectional Control Flow on Persona-Enabled Webpages Web Persona Triggers actions of the Persona Triggers operations on elements of the webpage

© W. Wahlster Plug-Ins Add features (character players) to browser Download triggered by user Requires disk space on client Unrestricted access to client Less appropriate for WebCommerce, Guides Agents integrated in 3D environments Appropriate for Entertainment Examples: Extempo's Jennifer James (Hayes-Roth et al. 98) PFMagic's virtual petz New in AiA/PAN: Balanced combination of Applets and Servelets Efficient distribution of client-side Java and server-side Java for driving the Interface Agent Sending Interface Agents to Clients: Plug-Ins or Applets? Applets Java animation code sent over the net Automatic loading Requires no disk space on client Restricted access to client Appropriate for WebCommerce, Guides Agents integrated in 2D environments Less appropriate for Entertainment Examples: DFKI's Web Persona (Müller et al. 98) ISI's Adele (Johnson et al 98)

© W. Wahlster Porsche 9 11 & Boxter

© W. Wahlster some HTML elements  Active Images An active image starts a persona action when clicked.  Addressable Objects An addressable object is an object which can be addressed and manipulated by Persona via its name and its position. Persona Active Elements (PAE)

© W. Wahlster A Virtual Sales Agent for OTTO – World’s Largest Tele-Ordering Company

© W. Wahlster WML- Browser MS-Agent Controller WML SMIL Agent Script PET- PML PET Persona Player SMIL Player Presentation Planner DFKI’s Ecommerce Presentation Planner has been extended to accommodate for various target platforms through the introduction of a mark-up language layer

© W. Wahlster Simulated Dialogues as a Novel Presentation Technique  Presentation teams convey certain rhetorical relationships in a more canonical way Provide pros and cons  The single presenters can serve as indices which help the user to classify information. Provide information from different points of view, e.g. businessman versus tourist  Presentation teams can serve as rhetorical devices that allow for a continuous reinforcement of beliefs involve pseudo-experts to increase evidence

© W. Wahlster Presentation Teams for Advanced ECommerce I recommend you this SLX limousine.

© W. Wahlster Underlying Knowledge Base  Representation of domain FACT attribute car_1 consumption_car_1  Value dimensions for cars adopted from a study of the German car market safety, economy, comfort, sportiness, prestige, family and environmental friendliness FACT polarity consumption_car_1 economy negative  Difficulty to infer implication of dimension on attribute FACT difficulty consumption_car_1 economy low

© W. Wahlster Example of a Dialogue Strategy Question: How much gas does it consume? Answer: It consumes 8l per 100 km. Negative Response: I’m worrying about the running costs. Dampening Counter: Forget about the costs. Think of the prestige! Header: (dampening_counter ?agent ?prop ?dim) Constraints: (*and* (positive ?agent) (pol ?prop ?other_dim positive)) Inferiors: (Speak ?agent (“Forget about the ” ?dim “!”)) (Speak ?agent (“Think of the ” ?other_dim “!”))

© W. Wahlster Multiple Interface Agents for User-adaptive Decision Support... weighted propositions User-Adaptive Search Planning Multiple Decision Support Agents But, it’s fast! Spare parts for this car are rather expensive!

© W. Wahlster MAUT (Multi-Attribute Utility Theory) - I  formalism for the evaluation of structured objects  basic idea: identification of relevant dimensions for the evaluation of an object  total evaluation of an object :  evaluation of the object regarding the dimension :  definition of the relationships between dimensions and attributes within the ontology  decision support: connection between currently focused data and user preferences

© W. Wahlster MAUT (Multi-Attribute Utility Theory) - II hotel cheapness culture sportiness   golf   tennis   price user´s interest on the dimension dimension relative weight of an attribute for the dimension evaluation function

© W. Wahlster MAUT - Example  hotel 1: tennis golf price: 5000 DM  hotel 2: tennis price: 2000 DM  

© W. Wahlster Research Topics: Multiple Interface Agents  Interactive Presentation Teams  Corpus-based Approach to Gesturing  Empirical Evaluation of Presentation Teams

© W. Wahlster Non-Interactive Presentation Teams I recommend you this SLX limousine.

© W. Wahlster Characteristics of the Interactive Presentation Scenario I  Character-Centered Approach Story is not defined by a script, but by the character‘s role, personality, status, attitude etc.

© W. Wahlster Characteristics of the Interactive Presentation Scenario II  Open Architecture New agents can join at any time  Auto-Progression Story unfolds no matter whether the user actively participates or not  Handling of Barge-ins Agents may interrupt each other at any time  Computer-Moderated Dialogue Meta-agent makes sure that all agents follow an agreed-upon interaction protocol

© W. Wahlster System Architecture for Miau Multi-Party Dialogue Scenario Jam BDI Client Spin: Template- based NL Analyzer Agent Server Agent Handler JIMPRO Goal BoardDialogue Protocol Dialog Management

© W. Wahlster Multi-Agent Dialogue Control... What can I do for you? Is it my turn? Competence Status Personality Is it my turn? Competence Status Personality   

© W. Wahlster Layer Model for Multi-Party Conversation Client-Server Communication Low-Level Synchronization and Coordination Strategies Character-Character & User-Character Communication Multimodal Dialogue Strategies (Speech, Gestures, etc.) Improvisational Setting Characters’ Roles, Status, Goals etc. Domain-Specific Communication Sales Strategies etc.

© W. Wahlster From Script-Based Approaches to Interactive Performances

© W. Wahlster Evaluation of Presentation Teams Does the number of agents in a presentation team affect the user’s ability to decode and retrieve information, and/or her affective judgement of the dialogue?

© W. Wahlster Formulation of Hypotheses  According to the levels of processing theory [Craik & Lockhart, 72], the additional spatial and episodic organization of discourse elements implemented by multiple agents should support the user’s retrieval performance.  Based on earlier experiments by [Nass 96], we expect that agents are rated more competent if specific areas of knowledge are assigned to them.  We also expect that the affective rating of the product increases with the observed competence of the presenters.

© W. Wahlster Settings for the Scheduled Experiment

© W. Wahlster Dialogue Examples for the Experiment I wonder, how fast this car might be? 2. Built-in servo-steering keeps the car easy-to- handle. 1. Maximum speed is 200 km/h. 1. Built-in servo-steering keeps the car easy-to- handle. 2. Maximum speed is 200 km/h. Multi-party dialogue, structured Multi-party dialogue, random -Maximum speed is 200 km/h. MonologueDialogue I wonder, how fast this car might be? -Oh, I see 200 km/h.

© W. Wahlster Personalized Sales Dialogues with Presentation Teams in the Miau System

© W. Wahlster Information Extraction Agents Information Filtering Information Retrieval Information Integration identify relevant documents wrappers –... – identify and extract relevant pieces of information – transform them into canonical form wrappers operational descriptions of a target concept abstract from concrete occurrence within document robust against modifications

© W. Wahlster The Trainable Information Agents Framework (Bauer, Dengler) Browser Application InfoBroker Info Extraction Trainer planning knowledge user preferences domain ontology Web site annotations User requests training specifications results info requests info info requests info or script PBD dialog preferences/heuristics site info/update site information combination of "classical" problem-solving methods and information agents query planning, optimization, and execution improved dialog guidance

© W. Wahlster DB Interface Objects Stereotypes Search Interface Result Abstraction User Model Refinement User Modeling Interaction Management Ontology Argument Generator Mediator Query Generator Strategic Planner UMs Conflict Res. Str. Catalog Presentation Overall Architecture

© W. Wahlster Ontological Reasoning for Decision Support: Topic Maps  specification for Topic Maps: XML Topic Maps (XTM) 1.0  description of complex relationships with associative knowledge structures  goal-oriented search and navigation in large data sets basic technology for the construction of knowledge structures key for knowledge management  flexibility by separation of maps and information resources  key concepts: topics, associations, and occurrences

© W. Wahlster Domain Theory  description of the general world knowledge (logical) representation of objects, their properties, and  the relations among themselves inference mechanism near($sport, $hotel, $city, 0-5 km) is_a(“Munich”, city) is_part_of(“Munich”,”Bavaria”), is_part_of(“Bavaria”,”Germany”)  is_part_of(“Munich”,”Germany”)

© W. Wahlster Ontological Reasoning for Decision Support: Topic Maps - I  objective: generating suitable queries for the object catalog Web information search  specifications for Topic Maps: ISO/IEC standard (1999), XML Topic Maps (XTM) 1.0  description of complex semantic relationships with associative knowledge structures  goal-oriented search and navigation in large data sets basic technology for the construction of knowledge structures key for knowledge management in a conflict case

© W. Wahlster Ontological Reasoning for Decision Support: Topic Maps - II  flexibility by separation of topic maps and information sources: application of the same topic on different sources application of different topic maps on the same source  difference between pure XML-documents and topic maps: for application of topic maps no annotation of the available data necessarily XML-document: view of the author on the knowledge structure topic map: view of the user on the knowledge structure and the associations with the available data

© W. Wahlster Ontological Reasoning with Topic Maps Example - I Interests of user A 60% 30% 10% Wellness Culture Sport Interests of user B Sport  conflict situation: user A found a suitable hotel in the catalog the hotel doesn’t offer sport possibilities for user B  objective: generation of a suitable Web query in order to receive information about the surrounding area of the hotel sport club fitness center footballtennisdiving school diving certificate offers concentrates on teach issues a certificate sport club fitness center footballtennisdiving school diving certificate offers concentrates on teach issues a certificate Topic Map

© W. Wahlster Ontological Reasoning with Topic Maps Example - II  situation: user found different suitable hotels in the area of: Costa Brava Costa Blanca Costa Dorada Cote d’Azur  objective: generation of a goal-oriented query for the object catalog in order to find additional hotels SpainFrance Costa Brava Costa Dorada Costa Blanca Cote d’Azur mild climate Mediter- ranean Ocean Atlantic Ocean SpainFrance Costa Brava Costa Dorada Costa Blanca Cote d’Azur mild climate Mediter - ranean Ocean Atlantic Ocean Topic Map

© W. Wahlster Ontological Reasoning for Decision Support: Topic Maps - III  key concepts: topic and topic type topic association and association type topic occurrence and occurrence role  additional concepts: scope and theme facets  topic map as knowledge structure Information pool

© W. Wahlster Programming by Demonstration - I  code repair  canonical representation by HTML parse tree  training dialog with the user  characterization of document units in terms of (generalized) HTML concepts  generation of HyQL scripts wrappers for information extraction from (static) HTML documents

© W. Wahlster Programming by Demonstration - II new requirements caused by dynamic documents  DHTML, JavaScript/JScript,...  documents only virtually available  canonical representation by DOM  flexible characterization of document units  more powerful extraction wrappers  more flexible training dialog

© W. Wahlster Programming by Demonstration - III  enhanced reasoning capabilities of learning agent +  DOM representation  multiple views of document and training task HTML objects, semantic concepts, spatial relationships,... flexible user-agent interaction user chooses preferred characterization agent translates into canonical representation and picks "most natural" interaction style with user  cooperation with MERL (COLLAGEN architecture)

© W. Wahlster HTML document dynamic HTML, JavaScript, CSS,... pure HTML PbD Agent static HTML treestructure  Generally, no equivalence between current DOM of browser and limited DOM of wrapper  DOM of wrapper is base for appropriate PbD heuristics and data model for wrappers written in HyQL  Problems concerning visual and structural correspondence of objects  Strictly server-based wrapper evaluation browser- specific DOM PbD-based Wrapper Construction for Information Agents

© W. Wahlster HTML document dynamic HTML, JavaScript, CSS,... PbD Agent browser- specific DOM  wrapper component and PbD Agent become integral part of browser  direct access to current DOM Additionally,  specialized data structures for additional reasoning, e.g. spatial information of DOM objects, semantic categories associated with DOM objects  server-side processing of wrappers by hosting the enhanced browser control component  debugging of wrappers easily supported General architectural decision: „A wrapper becomes part of the page which is the resource for its operation“ New approach to wrapper construction

© W. Wahlster Hypothesis: context/style $1 is image $2 is simple text H1 is bold text spatial relation H1 above $2 $1 left to H1 $1:width << $2:width topology $1 meets H1 H1 meets $2 $1 $2 H1 Query for all pairs ($1,$2) where $1:image H1:bold text $2:simple text Sample Wrapper Generation using PbD

© W. Wahlster Apply the same wrapper to a new source

© W. Wahlster Apply the same wrapper to a new source (2nd case)

© W. Wahlster PAN-Video

© W. Wahlster High Degree of Parallelism of Queries

© W. Wahlster structural visual/semantic procedural Naive User Learning Annotation Agent common part (usable for communication) Knowledge about a Webpage Shared by User and Agent

© W. Wahlster Train_Connection [ from =>> Location; to =>> Location; travel_date =>> Date; time =>> Time; depart_time =>> Time; arrive_time =>> Time; cost =>> Price; travel_duration =>> Duration; info_url =>> URL;... ] Example - Ontology

© W. Wahlster  states: information states concepts / attributes and instantiations  operators: querying schemes preconditions (´+´) and effects (´-´) to time arrive_time travel_duration from travel_date depart_time cost info_url Query Planning

© W. Wahlster City.value = München City.language = German... State CityName1 City value language Language Top String... Ontology + + – – babelfish Operators  opprec  cc  ciSi::: 0 )( 0 opIntS  Query Planning

© W. Wahlster Features alternative queries past states future states state descriptions PBD requests accept / reject PBD request assessment of plans expected completion time Query Plan Visualization

© W. Wahlster Three Levels of Mark-up Languages for the Web Content : Structure : Form = 1 : n : m WWW Document Content Structure Form OIL/M3L XML HTML

© W. Wahlster Frame Languages Object-oriented Modelling Primitives Concept Languages/ Terminological Logics Formal Semantics Subsumption, Inferences Web Languages XML and RDF Syntax M3L M3L Integrates Three Language Families

© W. Wahlster RuleML: Ontology Extensions for Rule Knowledge  Rules in the Web have become a mainstream topic since inference rules were marked up for E-Commerce identified as a Design Issue of the Semantic Web transformation rules were used for document generation from central XML repository  Rule interchange is becoming more important in Knowledge Representation (KR), especially for Intelligent Agents in E-Commerce

© W. Wahlster The Rule Markup Initiative  The Rule Markup Initiative has taken initial steps towards defining a shared Rule Markup Language (RuleML) for interoperation between companies  RuleML permits forward (bottom-up) and backward (top-down) rules in XML for deduction rewriting further inferential-transformational tasks

© W. Wahlster From traditional XML Representation to RDF-like Representation of RuleML Rules  XML: N-ary, positional representation of rules; overspecification for non-sequential parts  RDF: Binary, labeled representation of rules with nodes for resources and labels as explicit role names; Seq container needed for sequential parts  RuleML: Sequential parts from XML Labeled parts from RDF

© W. Wahlster You want to review rule principles You may look at Rule-Based Systems Recommender Rule: Forward Markups Original RuleML markup with XHTML in body/head (English premise and semiformal conclusion): Challenge hypertext as one XHTML paragraph: If you want to review rule principles, you may look at Rule-Based Systems premise conclusion

© W. Wahlster Recommender Rule: Backward Markup may look at you Rule-Based Systems want to review you rule principles Further formalized RuleML markup (still unanalyzed English relation and individual-constant names): conclusion premise

© W. Wahlster imp * * head * body * * * atom and * | | | | opr * | | | | * | | | | rel var var atom atom * | | | * | |... opr * | | | opr * | |... * | | | * | | own person object rel var var var rel var var buy person merchant object keep person object own person object buy person merchant object keep person object An Example coded in RuleML 08 A person owns an object if that person buys the object from a merchant and the person keeps the object.

© W. Wahlster Two-Way Relationship Between RuleML and RDF  RDF in RuleML: RDF triples as facts described by a DTD in the RuleML family Example: Next slide  RuleML in RDF: RDF graphs and serializations for RuleML rules Exemplified in previous slides

© W. Wahlster Intelligent Web Services Personalized Interface Agents User Modeling Planning Natural Language Understanding Knowledge Representation Image Understanding Machine Learning Plan Recognition Information Retrieval Multimodal User Interfaces Research on Personalized Interface Agents brings disparate subfields in the area of intelligent systems together

© W. Wahlster The generation of virtual webpages can be achieved by plan-based internet agents. Ontological annotations are needed not only for information extraction agents but also for presentation agents Realization procedures and wrappers form an important part of the referential semantics of objects on the web see Conclusion ECommerce projects of DFKI have shown that research on personalized interface agents can be transferred to real world applications: Dekra (largest European organization of used car dealers): FairCar as an ECommerce platform with NL access and a comparison shopping agent for used cars DaimlerChrysler: online user modelling in a one-to-one marketing system for Mercedes cars Porsche: Virtual Market for Pre-owned Porsche Cars

© W. Wahlster URL of this Presentation: