1. Mike Anderson, Darsana P. Josyula, Don Perlis
Talking to Computers
Mike Anderson, Darsana P. Josyula, Don Perlis
Active Logic, Metacognitive Computation and Mind Research Group, University of Maryland

2. Outline of Presentation
Mistakes and Meta-Reasoning
Conversational Adequacy
HCI--Our Approach
Example-reference resolution
Accomplishments
Future Research

3. I. Mistakes and Meta-Reasoning
Conversation is error-prone.
Monitoring and discussing the ongoing conversation (grounding) is an important resource for dialog repair.
The ability to ask about words and concepts is central to learning

4. II. Conversational adequacy
Is the ability to engage in free and flexible conversation.
Meta-dialog is necessary to conversational adequacy
Meta-dialog can make up for deficiencies in other areas of linguistic ability (Perlis, et al., 1998)

5. III. HCI-Our Approach Objective ALMA (Active Logic MAchine) Approach
An error tolerant, flexible and user-sensitive natural-language interface.
Approach
Metareasoning using active logic to detect dialog anomalies, and manage appropriate responses, including learning new words.
ALMA (Active Logic MAchine)
(i) reasons with contradictory data;
(ii) retracts old conclusions; and
(iii) reasons about its own history including changes in its beliefs. 

6. Reference resolution without meta-reasoning
IV. Example
Reference resolution without meta-reasoning
User: Send the Chicago train to Philadelphia.
[System sends a train that has just arrived in Chicago, to Philadelphia.]
User: No; send the Chicago train to Philadelphia.
[System resets itself and then sends the same train as before.]
** Inappropriate repetition of mistake **

7. Reference resolution with autonomous meta-reasoning
IV. Example (2)
Reference resolution with autonomous meta-reasoning
User: Send the Chicago train to Philadelphia.
[System sends a train that has just arrived in Chicago, to Philadelphia.]
User: No; send the Chicago train to Philadelphia.
[System resets itself and then sends the train that has been at Chicago overnight.]
** System recognizes its mistake, and alters its behavior appropriately **

8. Reference resolution with interactive meta-reasoning
IV. Example (3)
Reference resolution with interactive meta-reasoning
User: Send the Chicago train to Philly.
System: What is Philly?
User: Philly is Philadelphia.
System: OK.
[System sends a train that has just arrived in Chicago, to Philadelphia.]
User: No; send the Chicago train to Philly.
[System resets itself and then sends the train that has been at Chicago overnight.]

9. IV. Accomplishments ALFRED can: Maintain context and history
Introspect
Identify miscommunications
Generate utterances
Use meta-dialog
Resolve ambiguous references
Learn new words
Understand the use-mention distinction
Connect to different domains.

10. ALFRED: Active Logic for Reason-Enhanced Dialog
User
Task-Oriented Interactive System
Link Parser
Domain Controller
Alfred Modules
Command Detection
Expectations Handling
Learning
Intention Discernment
Reference Resolution
Context based Parsing
Ontology Tracking
Error Handling
Needs Handling
Introspection
Contradiction Handling
Utterance Generation
Alfred knowledge base (KB)
Domain Commands
Context
Alfred (Internal) Commands
Domain objects, relationships and associations
Names, relationships and associations
ALFRED: Active Logic for Reason-Enhanced Dialog

11. V. Future Research Metacognitive Loop Specific Dialog Projects
1. Note an anomaly has occurred
2. Assess the anomaly
a. characterize its type
b. assemble options for addressing it
3. Guide one or more of the options into place
a. choose option(s) to use
b. monitor (and control, when needed) the performance of the option(s).
Specific Dialog Projects
Learn new concepts
Generate and track dialog expectations and structure
Corpus study on the frequency and types of meta-dialog in conversation

12. Mike Anderson, Darsana P. Josyula, Don Perlis
Talking to Computers
Mike Anderson, Darsana P. Josyula, Don Perlis
Active Logic, Metacognitive Computation and Mind Research Group, University of Maryland