Conversation as Action Under Uncertainty Tim Paek Eric Horvitz

Abstract Conversation  uncertainties of various kinds Quartet architecture  Inference/Decision making under uncertainty  4 levels of analysis  Representations  Inference procedures  Decision strategies Interaction between a user and dialog system

1. Introduction Spoken dialog system  robust, unified architecture for sharing key uncertainties  Quartet

2. Toward an Architecture for Conversation General conversation  To make themselves understood  Uncertainty: communication failure  Collaborate/establish mutual belief  “feedback” = coordination Head nod, “uh huh” Clear up uncertainties, take other measures Conversation = collaborative effort or joint activity

Grounding  상호 이해를 위해 대화의 presentation 또는 acceptance 를 coordinate 하는 과정  Consideration of how key uncertainties depend on each other and influence mutual understanding  What decisions to make in light of these uncertainties

Quartet  Treat grounding as decision making under uncertainty  Key uncertainty  Bayesian Network  Local expected utility/VOI Identify action  maximize mutual understanding  Focus on handling uncertainties  Enable dialog system to adapt strategies  Allow components to share uncertainties

2.1 Four Levels of Analysis Evaluate grouding at 4 levels of analysis 4 Levels  Channel level  Signal level  Intention level: signal 의 semantics 이해  Conversation level All levels need coordination/collaboration Unified architecture  Model probabilistic dependencies between levels

2.2 Models, Inference, Decisions atemporal/temporal Bayesian networks  compute likelihood of variables of interest  Ex) overall grounding status

2.2.1 Modularity Maintenance module  handle uncertainties about channel and signal levels Intention module  About intention level Conversation control  Keep track of the grounding status  Where to focus on resolving uncertainties  What grounding actions to take in light of their likely costs/benefits

Reasons for distinct modules  Probabilistic dependencies abundant with the modules sparse between them  Enhancement of the flexibility of Quartet To be applied to variety of task domains  Belief, decision, degree of detailed necessary for each level varied significantly depending on the communication medium or modality

2.2.2 Representation for Decision Making Grouding status  Diagnoses the overall mutual understanding

2.2.3 Refinement through Value of Information Need to refine actions by sharing uncertainties Utterance design  improve understanding Conversation  decision making  Capitalize on VOI analysis to ask questions, make recommendations, and seek out information

 VOI analysis find best evidence in light of the inferred probabilities  Compute VOI  모든 observation 에 대해 expected utility of the best decision 을 계 산  VOI recommend a piece of evidence to observe: system 이 recommendation 제공 또는 phrase a question

 Exact computation of VOI  all possible sequences of observations  Greedy VOI: reasonable approximation

3. Runtime Behavior Dialog system  ASR  NLPWin parser  Face-pose tracker

3.1 Providing Services 완전 이해후의 highest expected utility action  User 와 requested action 을 연관시키는 것

3.2 Exploiting Multiple Levels Goal 이 not so clear at the intention level  Consider uncertainties at lower levels of analysis

3.3 Adapting Strategies over Time Mutual understanding 이 fluctuate  adapt strategy over time Do requested service / resort to only three repair strategies  ASK REPEAT, CONFIRM, TROUBLESHOOT (use VOI analyses to recommend procedures to improve mutual understanding)

4. Conclusions Approach to continuous spoken dialog centering on an architecture called Quartet