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Conversational Agent

Conversational Agent. Two layers: Dialogue manager and Conversational agent. Rule-Based Translator (ELIZA and PARRY) 3. Layer one: Dialogue Manager. Dialogue Modeling. What does MARINA achieve?. 5. Level two: Conversational Agent 6. Other agents. MARINA in context.

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Conversational Agent

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  1. Conversational Agent • Two layers: Dialogue manager and Conversational agent. • Rule-Based Translator (ELIZA and PARRY) 3. Layer one: Dialogue Manager. • Dialogue Modeling. What does MARINA achieve?. 5. Level two: Conversational Agent 6. Other agents. MARINA in context. 7. Questions and answers.

  2. Marina’s Goal • Assist students learning a particular language. • Propose topic in a foreign language. The Conquest of Mexico. • Different agents will have different characters in the story. • There will be a helper agent who will give hints on what task is next. • This agent may provide sources such as web pages, photos, audio files for students to look up and figure out what the story is about.

  3. Conversational Agent (Proposition Based) Knowledge Store (current propositions) Situated Actions Conversational Agent and Dialogue Manager Bindings & FSA Dialogue Manager (FSA) Bindings

  4. Agent Knowledge Store Situated Actions Conversational Agent • Provide interface through Instant Message or Email. • Communicate with students and other agents. • Choose a dialogue network to feed the Dialog Manager.

  5. Dialog Manager: Manager (FSA) • Explore dialogue network. • Use pattern matching and rule-based translator techniques. • Return a set of values required by later dialogues.

  6. Pattern matching and Rule-based translator. ELIZA Men are all alike. IN WHAT WAY They’re always bugging us about something. CAN YOU BE MORE SPECIFIC. Well, my boyfriend made me come here. YOUR BOYFRIEND MADE YOU COME HERE. He says I’m depressed much of the time. I AM SORRY TO HEAR YOU ARE DEPRESSED.

  7. [282-8992] Ok, thanks!! [yes] 4 Good, Do you have M’ number? [xxx-xxx] 1 [--] Ok, thanks!! 5 Good Bye. Is this X? 8 0 [yes] 6 What is it?? 3 [--] Do you have Martha’s number? [--] 7 Hmm, I think Martha sent you an email.. 2 [no] Ok, I’ll check later. FSA for Telephone Request [--] Any response

  8. Dialogue Modeling.What does MARINA achieve? • Turn taking (Rules) • Adjacency pairs (greeting-greeting, request-grant, request-reject, etc) • Grounding (continuer, back channel, acknowledgement, request for clarification) • Implicature (Questions answered indirectly, commands presented as questions, etc) Maxims of quantity, quality, relevance and manner. Belief, Desire and Intention

  9. Agent KS SA Level two: Conversational Agent • The situated action region is a set of all possible actions that the agent can perform. • The knowledge store is the region where true propositions are stored. A situated action has three components: Situation: [[‘Ask’, ‘student’, ‘Telephone number’] .[]..] Action: [[IM, ‘student’, ‘FSA.file’] ….[] …] Results: [[ADD, [‘contact’, ‘student’, ‘pending’]] …]

  10. Other agents. Marina in context • Nuance Communications’ Dialog Builder • CSLU speech toolkit (center for spoken language understanding) • Philips Train Timetable System • SRI-Autoroute • Trains • Verbmobil

  11. Marina Conversational Agent is particular in: • Its application is language learning. • Simplicity since it processes text to text • It provides the language learner with tireless conversational partners • It provides AOL instant message like environment • It also supports email communication • It provides a virtual language community of agents and users

  12. Questions and answers.

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