Multi-party, Multi-issue, Multi-strategy Negotiation for Multi-modal Virtual Agents

1. Multi-party, Multi-issue, Multi-strategy Negotiation for Multi-modal Virtual Agents Santosh Sugur 10/25/2011

2. Dr. David R Traum • Research Assistant Professor, USC • Institute for Creative Technologies, USC. • PhD CS , U Rochester • Natural Language Processing.

3. Stacy Marsella • Research Associate Professor Computer Science, USC • Co-Director USC Emotion Group • Associate Director, Institute for Creative Technologies – Social Simulation • B.A. in Economics from Harvard University • Ph.D. in Computer Science from Rutgers University. Focus on AI planning, human problem solving and cognitive science

4. Jonathan Gratch • Research Associate Professor, USC • Associate Director, Institute for Creative Technologies - Virtual Human Research • Co-Director, USC Computational Emotion Group • Ph.D. in Computer Science, University of Illinois, June 1995 • Joined ICT in August 1995

5. Jina Lee • Phd Student under Prof. Stacy Marsella Arno Hartholt • Project leader of the Integrated Virtual Humans group and Central ICT Art Group. • BS, MS, University of Twente, Netherlands • Joined ICT in 2005

6. Negotiations“Never cut what you can untie” • Using agents to train humans in negotiations • Communicate over multiple modalities – speech, gestures, body postures • Dynamic negotiations – change positions and strategies to achieve those goals.

7. How can a human trainee do well? • Solve problems • Gain Trust • Familiarity • Credibility • Solidarity • Manage Interactions

8. Multi-party Dialogue model • Maintain a snapshot of Dialogue State – called Information State. • Information state is updated by dialogue moves based on certain update rules. • Information state and dialogue moves are partitioned into layers each dealing with slightly different aspects.

9. Layers in the dialogue model • The contact layer concerns whether and how other individuals can be accessible for communication. Modalities include visual, voice (shout, normal, whisper), and radio. • The attention layer concerns the object or process that agents attend to. Contact is a prerequisite for attention. • The Conversation layer models the separate dialogue episodes that go on during an interaction.

10. Layers in the dialogue model contd. • The participants may be active speakers, addressees, or over hearers. • The turn indicates the (active) participant with the right to communicate • The initiative indicates the participant who is controlling the direction of the conversation • The grounding component of a conversation tracks how information is added to the common ground of the participants.

11. Additions to dialogue model • Listening (input) • Improved Gaze model • Increase in non verbal feed back during listening. • Talking (output) • Verbal and non verbal behavior also takes into account where the negotiator’s views are coming from / larger understanding of the issue – helps in negotiation decisions. • Dynamically update motivations for strategies based on where the negotiations are at.

12. Multi Party Negotiation Strategies • Apart from disagreements on topics, people may simply dislike each other which is not considered. • Find Issue • Avoid • Attack • Negotiate • Advocate • Success • Failure

13. Factors involved in strategy selection • Topic • is there a topic? – no - find issue() • Is it what I want? – no - avoid() • Can I work with topic? Yes – negotiate() • Control • Can I control the discussion • I need to control if I have to avoid the topic • Utility • High utility? Advocate() • Absolute utility of issue • Relative utility compared to other options

14. Factors involved in strategy selection • Potential • How good or bad can the utility get, should I attack or negotiate • Trust • Do I trust the other interlocutors • Commitments • Positive commitments are a required for a success strategy • Negative commitments result in failure

15. Multi-issue Utilities • Agent actions (dialogues) cause agents to achieve their goals. • Agents are aware of the goals of other agents • Also aware of actions that can achieve or thwart those goals. • In negotiations try to weigh different action roadmaps to achieving goals. • Choose a plan (roadmap) based on positive utility and the probability that the plan will be executed. • Don’t choose a plan if it has negative utility and flaws that will block its execution

16. Expected and Potential Benefit • Expected benefit is a calculation based on commitments and trust. • Potential Benefit is the benefit assuming that the plan will succeed. • A plan with high potential benefits help to negotiate by advocating that course of action.

17. Choosing negotiation strategies based on factors

18. Different possible actions for a chosen strategy • Find Issue • Propose a topic • Request a topic • Initiative parameter dictated which action to take • Avoid strategy • Change topic to high utility • Talk about non-issues • Disengage from meeting if there is some control

19. Different possible actions for a chosen strategy • Attack strategy • Ad hominem attack • Point out flaws in issue • Negotiate • Attack • Propose solutions • Advocate • Talk about high utility outcomes • Address flaws • Offer / solicit commitments

20. Failed negotiation example

21. Successful negotiation example

22. Discussion points • What could be some of the things that could be used in a human to human negotiation, that cannot be used with virtual agents. • Eg. Making a connection based on similar past experiences between interlocutors and using that to your advantage. • Can such a training setup replace real negotiation experiences?