Multimodal Interaction for Distributed Interactive Simulation Philip R

Multimodal Interaction for Distributed Interactive Simulation Philip R. Cohen, Michael Johnston, David Mc. Gee, Sharon Oviatt, Jay Pittman, Ira Smith, Liang Chen and Josh Clow Center for Human Computer Communication Oregon Graduate Institute of Science and Technology http: //www. cse. ogi. edu/CHCC Presenter: Keita Fujii

Overview • Background: Military simulation – Leather. Net • Quick. Set: Multimodal interface for simulation – Architecture – Technical Issues • Gesture recognition • Multimodal integration • Agent infrastructure – Lessons learned

Background • U. S. government is developing large-scale military simulation capabilities – >50, 000 entities (e. g. , a vehicle or a person) in a simulation • Leather. Net – Virtual simulation system for training platoon leaders and company commanders – Based on Mod. SAF (Modular Semi-Automated Forces) simulator – Support Command. Vu • Wall-sized vertual reality

Required Interface • Simulation interface should provide the following operations – – Create entities Supply their initial behavior Interact with the entities Review the results • Simulation interface should be – Multimodal: # of entities is large – On a portable size device: for mobility and affordability

Quick. Set • Quick. Set – – Multimodal interface for Leather. Net Offers speech and pen-based gesture input Runs on 3 -lb hand-held PC Based on Open Agent Architecture

Architecture Quick. Set Interface Mod. SAF Simulator Multimodal integration agent Speech recognition agent Gesture recognition agent Natural language agent Simulation agent Web display agent Open Agent Architecture CORBA bridge agent CORBA Architecture Command. Vu agent Application bridge agent

Architecture Quick. Set Interface Gesture recognition agent Mod. SAF Simulator Multimodal integration agent Speech recognition agent Natural language agent Simulation agent Command. Vu agent • Quick. Set interface Web – Draws map, Open icons, entities Agent Architecture display – Activates speech and gesture recognition agent when the pen is placedagent on the screen • Speech recognition agent CORBA – IBM’sbridge Voice. Type Application Factory agent • Gesture recognition agent Application bridge agent – Analyzes pen input and gives N-best list of possible interpretations CORBA Architecture

Architecture Quick. Set Interface Gesture recognition agent Mod. SAF Simulator Multimodal integration agent Speech recognition agent Natural language agent Simulation agent • Natural language agent Command. Vu agent Web – Analyzes a. Open natural language input from speech recognition agent and Agent Architecture display agent produces typed feature structures • Multimodal integration agent – CORBA bridge Accepts typed agent Application from bridge the language agent feature structures agent and the gesture agent, unites those structures together and produces a multimodal interpretation CORBA Architecture

Architecture Draw map and entities Quick. Set Interface Gesture recognition agent Mod. SAF Simulator Multimodal integration agent Speech recognition agent Natural language agent Simulation agent • Simulation agent Command. Vu agent Web – Serves as the communication channel between OAA agents and Open Agent Architecture display agent Mod. SAF simulation system • Command. Vu agent CORBA alsobridge implemented agent Application thatbridge the same agent – Is as an agent so multimodal interface (speech and gesture) can be used in Command. Vu CORBA Architecture

Architecture Draw map and entities • Application bridge agent Quick. Set Mod. SAF Multimodal such as Mod. SAF, Simulator Interface – Bridges APIs of the various applications, Command. Vu integration agent • Web display agent – Allows a user to manipulate Mod. SAF simulation through Java applet on a Simulation Speech WWW browser agent Gesture • CORBA bridgerecognition agent recognition – Onverts OAA messages agent to Natural language CORBA agent IIOP/GIOP Web display agent Open Agent Architecture CORBA bridge agent CORBA Architecture Command. Vu agent Application bridge agent

Gesture recognition • Quick. Set’s pen-based gesture recognizer – Consists of neural network and hidden Markov models – Combines results from two recognizers • To yield probabilities for each of the possible interpretations Neural Net Route: 0. 4 Area: 0. 2 Tank: 0. 01 combine hidden Markov model Gesture Route: 0. 7 Area: 0. 1 Tank: 0. 1 Route: 0. 6 Area: 0. 1 Tank: 0. 01

Multimodal integration • Based on unification operation over typed feature structures – If two pieces of partial information can be combined without loosing their consistency, combine them into a single result Speech recognition operation draw_line Line (10, 10)-(20, 20) Gesture recognition Point (10, 10) Line (10, 10)-(20, 20)

Agent infrastructure • Open Agent Architecture – All communication among the agents takes place through the facilitator agent • When an agent registers with the facilitator agent, it supplies a list of goals it can solve • Agents post goals to be solved to the facilitator agent • The facilitator agent forwards the goals to the agents that can solve them – Uses ICL (Interagent Communication Language) • Similar to KQML (Knowledge Query and Manipulation Language) and KIF (Knowledge Interchange Format) Goals Agents register Facilitator request Agents

Lessons learned • Open Agent Architecture – Does not provide features for authentication or locking • Prevents one user’s speech from being combined with another user’s gesture – Does not support multi-thread • Cannot support a large number of users – Centralized architecture of facilitator agent • Not scalable • Multimodal interface – Quick. Set proves that multimodal interaction offers the possibility of more robust recognition

ANIMATED CONVERSATION: Rule-based Generation of Facial Expression, Gesture & Spoken Intonation for Multiple Conversational Agents Justine Cassell, Catherine Pelachaud, Norman Badler, Mark Steedman, Brett Achorn, Tripp Becket, Brett Douville, Scott Prevost, Matthew Stone Department of Computer & Information Science University of Pennsylvania Presenter: Keita Fujii

Overview • Introduction • Background – Face expression – Hand gesture • System Architecture – Speech generation – Gesture Generation – Facial Expression Generation

Introduction • This paper presents “automatically animating conversations between multiple human-like agents” – With speech, intonation, facial expressions, and hand gestures – Those expressions are synthesized to make the agents look more realistic

Facial expression • Facial expression can perform – Syntactic functions • Accompanies the flow of speech – E. g. , nodding the head, blinking – Semantic functions • Emphasizes a word • Substitutes for a word • Refers to an emotion – E. g. , smiling and say “it is a NICE DAY. ” – Dialogic functions • Regulate the flow of speech – Mutual gaze for smooth conversation turns

Hand gesture • Hand gestures can be categorized into as – Iconics • Represents some feature of the word – E. g. , make a rectangular while saying “a CHECK” – Metaphorics • Represents an abstract feature/concept – E. g. , form a jaw-like shape with a hand pull it while saying “I can WITHDRAW fifty dollars” – Deictics • Indicates a point in space – E. g. , point to the ground and say “THIS bank” – Beats • Hand waves that occur with emphasized words etc – E. g. , wave a hand while saying “all right” • Hand gestures, facial expressions, eye gaze and speech need to be synchronized

System Architecture Dialog Planner Symbolic Gesture Specification World and Agent Model Symbolic Intonation Specification Speech Synthesizer Phoneme Timings Gesture and Utterance Synchronization Gesture Pa. T-Net Facial Pa. T-Net Movement Specification Animation System Graphic Output Sound

Speech Generation • Dialog planner – Generates dialogs • Based on the common knowledge, the agent’s goal, and its believes – Dialog includes • The timing of the phonemes and pauses • The type and place of the accents • The type and place of the gestures • Speech Synthesizer – Generates sound data from the dialogs

Gesture generation • Gesture is generated through three steps A) Symbolic Gesture Specification – Decides what type of gesture to use for each word B) Pa. T-Nets (Parallel Transition Networks) – Determines shape, position, transition and timing of gestures C) Gesture Generator – Generates actual motion from the information sent by Pa. T-Nets

Symbolic Gesture Specification • Determines the type of gesture – Words with literally spatial content (“check”) Iconic – Words with metaphorically spatial content (“account”) metaphoric – Words with physically spatializable content (“this bank”) deistic – Other new references beat – Also based on the annotations from the dialog planner and classification of reference (new to speaker and listener, new to speaker but not to listener, or old)

Pe. T-Nets • Pe. T-Net is a finite state machine – Each state represents an action to be invoked – State transition is made either conditionally or probabilistically – Thus, a state transition generates a sequence of actions • Gesture Pe. T-Net generates gestures, Facial Pe. T-Net generates facial expressions Send beat to beat Pa. T-Net Send gesture info to gesture Pa. T-Net Beat signaled parsing Gesture info found Get gesture info Gesture info complete

Coarticulation • The structure of Pe. T-Net allows coarticulation – Two gestures occurs without intermediary relaxations • I. e. , start the next gesture without waiting for the first one to finish – Coarticulation occurs when there is no sufficient time to finish a gesture Finish gesture B Finish gesture A pausing Start gesture A Start gesture B

Gesture Generator • The animation of a gesture is created as a combination of – Hand shape – Wrist control – Arm positioning – The system tries to get as close as possible to the gesture goals, but may fail because of coarticulation effects

Facial expression generation • Facial expression is generated through the same steps as gesture A) Symbolic Facial Expression/Gaze Specification – Decides what type of expression to use for each word B) Facial/Gaze Pa. T-Nets – Determines shape, position, transition and timing of gestures C) Facial Expression/Gaze Generator – Generates actual motion from the information sent by Pa. T-Nets

Symbolic Facial Expression/Gaze Specification • Symbolic Facial Expression Specification – Generates facial expressions connected to intonation • Symbolic Gaze Specification – Generates the following types of gaze expression • Planning – E. g. , look away while organizing thought • Comment – E. g. , look toward the listener when asking a question • Control – E. g. , gaze at the listener when ending speech • Feedback – E. g. , look toward the listener to obtain feedback

Pe. T-Nets • Facial expression Pe. T-Nets – No information in paper • Gaze Pe. T-Net – Each node is characterized by a probability • An action of a node is invoked probabilistically gaze feedback planning comment Short turn Within turn control Beginning of turn accent Back channel Utterance answer End of turn Utterance question Turn request Configuration signal

Facial Expression/Gaze Generator • Facial expression generator – Classifies an expression into functional groups • Lip shape, conversational signal, punctuator, manipulator and emblem – Uses FACS • Represents an expression as a pair of timing and type • Gaze and head motion generator – Generates motion of eye and head • Based on the direction of gaze, timing, and duration

Direct Manipulation vs Interface Agents Ben Shneiderman and Pattie Maes Interactions, Nov. and Dec. 1997 Presenter: Keita Fujii

Introduction • This article is about a debate session in IUI* 97 and CHI** 97 • Topic – Direct Manipulation vs Interface Agent • Speaker – Ben Shneiderman • From University of Maryland, Human-Computer Interaction Lab • Proponent of Direct Manipulation – Pattie Maes • MIT Media Laboratory • Proponent of Intelligent Agent * Intelligent User Interface Workshop **Conference on Human Factors in Computing Systems

Overview • Direct Manipulation • Software Agent – Benefits – Criticisms – Misconceptions • Objections to agent system • Agreement • Q&A

Direct Manipulation (Ben Shneiderman) • User interface using information visualization techniques that provides – Overview • How much /what kind of information is in the system – Great control • E. g. , zoom in, scroll, filter out – Predictability • User can expect what’s happing next – Detail-on-demand • Benefits – Reduce errors, and encourage exploration

Examples of Direct Manipulation • Film. Finder – Organizes movies in 2 D plane with years and popularity • Lifeline – Shows a case history graphically • Visible Human Explorer – Displays coronal section and cross sections of a human body

Software agent (Pattie Maes) • Software agent is the program that is – Personalized • Knows the individual user’s habits, preferences, and interests – Proactive • Provides or suggests information to user before being requested – Long-lived • Keeps running autonomously – Adaptive • Monitors the use’s interests as they change over time – Delegate • User can delegate some task to the agent • Agent acts on the user’s behalf

Examples of Software Agent • Letizia – Pre-loads web pages that the user may be interested in • Remembrance Agent – Remembers who sent email or whether email is replied • Firefly – Personal filters / personal critics • Yenta – Matchmaking agent – Introduces another user who shares the same interests

Benefits of software agent (Pattie Maes) • Software agents are necessary because – The computer system is getting more complex, unstructured, and dynamic • E. g. , WWW – The users are becoming more naïve • End users are not trained to use computers – The number of tasks to be managed with computer are increasing • Some tasks need to be delegated to somebody

Criticisms of agents (Pattie Maes) • Well-designed interfaces are better – Even if the interface is perfect, you may just not want to delegate some tasks to somebody • Agents make the user dumb – Yes, it’s true. But as long as there’s always an agent available, it’s not a problem • Using agents implies giving up all control – You don’t have to have full control. As long as your task is satisfactorily done, that’s fine – However, the system must allow user to choose between direct manipulation and task delegation to the agent

Misconceptions about agent (Pattie Maes) • Agents replaces user interface • Agents need to be personified or anthropomorphized • Agents need to rely on traditional AI They all are NOT true

Objections to Agent System and Responses to the Objections (Both) • “Agent” is not a realistic solution for making a good user interface because – Agent cannot be smart and fast enough to make some intelligent decision for human user • Direct manipulation is for – Professional users not for end users – Very well structured and organized domain not for ill structured and dynamic domain • Agent system can cooperate with Direct Manipulation – E. g. , Film. Finder with agent making movie suggestions

• Anthropomorphic interfaces/representation are not appropriate – Agents do not have to be visible • There is no “agents” in the Firefly Web site – “Agent” has a broader meaning than “software agent, ” so you need to distinguish different types of “agents” • Autonomous robots, synthetic characters, software agents etc

Direct manipulation & agent system (Both) • Agent is NOT an alternative but a complementary technique to direct manipulation (interface) – Agent system needs a good user interface that provides good understanding (overview) and control – Agent designer must pay attention to user-interface issues such as understanding and control • Two layer model – The user interface level • Predictable and controllable – The agent level • Adaptive, proactive system to increase usability Visualization / User Interface Agent System

Q&A • Q. How do speech technologies affect direct manipulation and agent system? – A. Speech won’t be a generally usable tool because • It disrupt cognitive process • Low bandwidth communication • Ambiguous – A. Speech can be used as a supportive medium

• Q. How can user interface and/or agent system support time-critical decision-support environment where mistakes are critical? – Agent system is not suitable for such system because it is very hard to make agents that never make mistake

• Q. How can we build a direct manipulation system for vision challenged or blind users? – Direct manipulation can be used to make an interface for such users because direct manipulation depends on spatial relationships and blind users often are strong at spatial processing • Q. What is it about agents that you dislike? (to Ben Shneiderman) – “intelligent agent” notion avoids dealing with interface issues, but this will be changed

So, where did they cheat? ? ?