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MATESMATES V.H. Allan: Utah State University 1 Marital Agent Trait-Based Emotion System System collects information about a pre- marital couple. Use questionnaires.

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Presentation on theme: "MATESMATES V.H. Allan: Utah State University 1 Marital Agent Trait-Based Emotion System System collects information about a pre- marital couple. Use questionnaires."— Presentation transcript:

1 MATESMATES V.H. Allan: Utah State University 1 Marital Agent Trait-Based Emotion System System collects information about a pre- marital couple. Use questionnaires to determine Personality- IPIP NEO Relationship concerns (Gottman) Goals with emotion vectors Model couple in negotiation

2 MATESMATES V.H. Allan: Utah State University 2 Idea: What if we programmed an agent to act like a person in a social situation? Could a person learn something valuable by seeing his behavior? Could a person benefit by replaying the situation using a new set of behaviours?

3 MATESMATES V.H. Allan: Utah State University 3 IPIP Personality Survey (Goldberg)

4 MATESMATES V.H. Allan: Utah State University 4 IPIP-NEO Narrative Report – (Validated Survey) EXTRAVERSION 94 Gregariousness 91 Assertiveness 82 Activity Level 97 Excitement-Seeking 49 Cheerfulness 80 AGREEABLENESS 81 Trust 87 Morality 63 Altruism 88 Cooperation 73 Modesty 25 Sympathy 77 Computed from 120 questions Includes explanation of Traits

5 MATESMATES V.H. Allan: Utah State University 5 IPIP-NEO Narrative Report (cont) NEUROTICISM 6 Anxiety 0 Anger 16 Depression 16 Self-Consciousness 39 Immoderation 8 Vulnerability 29 OPENNESS TO EXPERIENCE 35 Imagination 65 Artistic Interests 18 Emotionality 83 Adventurousness 9 Intellect 86 Liberalism 4 CONSCIENTIOUSNESS 84 Self-Efficacy 96 Orderliness 36 Dutifulness 87 Achievement-Striving 83 Self-Discipline 87 Cautiousness 7

6 MATESMATES V.H. Allan: Utah State University 6 Interaction Style Questions John Gottman’s Work I soften the conflict by constructively focusing on feelings first (i.e., "This is how I feel when...") and then moving on to the specific issue or complaint. I focus on one specific issue at a time and seek to resolve it before moving on to another issue so the conflict doesn't degenerate into a mudslinging contest. I focus on the present issue rather than bring up issues from the past as weapons to use in an attempt to gain power and control over the situation or another person. I think win/win and understand that if one person loses the argument, then both people in the relationship lose. I soothe my partner through speaking non-defensively, validating his or her perceptions and feelings, or by using humor. I seek to resolve the specific issue as soon as possible in order to avoid experiencing ongoing resentment, frustration, or hurt feelings.

7 MATESMATES V.H. Allan: Utah State University 7 Relationship Goal Questionnaire (Validity Untested)  I get satisfaction in making my partner happy.  I get satisfaction in controlling our relationship.  Getting my own way is important.  Having a positive interaction is important to me.  Being validated is important to me.

8 MATESMATES V.H. Allan: Utah State University 8 Program Agents with Personality and Emotion Bob and Alice are considering marriage. Evaluate their personalities Agent Bob and Agent Alice Give Agents a problem and view how they negotiate.

9 MATESMATES V.H. Allan: Utah State University 9 “My wife and I had words, but I never got to use mine.” -Fibber McGee Goal: Create better communication

10 MATESMATES V.H. Allan: Utah State University 10 Marital happiness is a function of both expectation and actual relationship quality. Goal: Create more realistic expectations

11 MATESMATES V.H. Allan: Utah State University 11 Marital Research How a couple differs is not so important (as there will always be differences). What is important is how they deal with those differences. This research seeks to expose differences.

12 MATESMATES V.H. Allan: Utah State University 12 Several studies suggest Researchers can predict which marriages will end in failure from information gathered before the couple marries. Tell people if they are at substantially greater risk for divorce Told couples argue most about children and money, but some believe how they argue is most important.

13 MATESMATES V.H. Allan: Utah State University 13 Inference Engine Plan GeneratorGoal Database Plan Emotion Transformer Behavior Generator Conversation Partner External Communication History Beliefs Personality Control Flow Data Flow Reactive Response Generator

14 MATESMATES V.H. Allan: Utah State University 14 Planner Generates Interaction Simple, hierarchical ordered planner Implemented in Prolog An agent calls the planner with a list of goals to obtain the plans. The agent calls the prolog function plan ([a, b, c], [], Plans) 1st argument: a, b and c are the goals. 2nd argument: specifies the initial list of plans to start with 3rd argument: Accumulator for the resulting plans.

15 MATESMATES V.H. Allan: Utah State University 15 Who determines the starting goal- list on which the planner works to generate plans? The human user (useful for testing agents’ behavior on several kinds of goals) An automatic goal initializer – –a simple piece of prolog code –evaluates the preferences

16 MATESMATES V.H. Allan: Utah State University 16 Three phases of planner Environment Setting phase Fact retrieval phase Presentation planning phase

17 MATESMATES V.H. Allan: Utah State University 17 Dynamic re-planning The basic idea is to plan for many expected situations and when an unexpected situation arises, modify the database and re-plan. Possible extensions to planner: Make it stochastic to simulate human indeterminism. Probabilities are determined by personality, emotion, and history.

18 MATESMATES V.H. Allan: Utah State University 18 Agent Database: For each agent: 1.Goals 2.Beliefs 3.History 4.Personality 5.Miscellaneous

19 MATESMATES V.H. Allan: Utah State University 19 Decomposing goal - precondition showPower(Proposal, Activity, ActivityName, ActivityActors, ActivityType, Time), [ logic(X), X < logicCutoff, polite(Y), Y < politeCutoff, likes(Activity), enjoys(ActivityType), not(partnerLikes(Activity)), not(partnerEnjoys(ActivityType)), activityName(Activity,ActivityName), activityActors(ActivityName,ActivityActors), free(Time) ],

20 MATESMATES V.H. Allan: Utah State University 20 Plan - components [ propose(Activity, ActivityName, ActivityActors, ActivityType, Time), [acknowledge(accept(Activity, ActivityName, ActivityActors, ActivityType, Time)); accuse(reject(Activity, ActivityName, ActivityActors, ActivityType, Time))] ] ).

21 MATESMATES V.H. Allan: Utah State University 21 Express interaction patterns as regular expression propose (reject cope)* (accept react)? * zero or more occurrences ? zero or one occurrences

22 MATESMATES V.H. Allan: Utah State University 22 Express interaction as stochastic context free grammar Used as a generator. Grammar to control options Stochastic to give probability to actions. Probability depends on history, personality, interaction patterns. Dynamically evaluated

23 MATESMATES V.H. Allan: Utah State University 23 A very simple plan might look like: // Environment setting phase setHonesty(minHonestyValue), setAffection(minAffectionValue), setPride(maxPrideValue), // Fact Retrieval phase getFreeTimes(self, FreeTimes), getFreeTimes(partner, FreeTimes) getActivity(Activity, FreeTimes), not PartnerLikes(Activity), // Presentation planning phase propose(Activity), acknowledge(accept(propose(Activity)), accuse(reject(propose(Activity)), replan(other).

24 MATESMATES V.H. Allan: Utah State University 24 Interaction Patterns speaker/listener (take roles) criticism defensiveness contempt stonewalling (listener withdrawal emotionally and perhaps physically) kitchen sink (prior complaints brought up)

25 MATESMATES V.H. Allan: Utah State University 25 Emotions to expression  Emotions passed as an internal form. –complete range of values –no need to parse expression for meaning –can filter so not “transparent”  Expressions are generated for GUI  Difficulty in mapping large number of emotions into expression.

26 MATESMATES V.H. Allan: Utah State University 26 Mapping – rejection phrases  Sorry.  I can’t.  I can’t. Maybe some other time  I’d rather not.  No.  I won’t  Absolutely not.  That is ridiculous. I won’t consider it.

27 MATESMATES V.H. Allan: Utah State University 27 Mapping - motivations Consider various reasons for saying no Conflict. Possible conflict No interest in going to event. Too busy. Anger over other rejections Feel person is inconsiderate

28 MATESMATES V.H. Allan: Utah State University 28 Mapping personalities How does personality affect answer? aggressive trusting cooperative cautious depressed anxious angry

29 MATESMATES V.H. Allan: Utah State University 29 In theory, mapping Personality History Plan output expression Very complex mapping Result

30 MATESMATES V.H. Allan: Utah State University 30 Regular expression Rejection: reject+ explanation* judgment* soften? counter-proposal? + one or more * zero or more ? zero or one Repetition determined by parameters

31 MATESMATES V.H. Allan: Utah State University 31 Explanation because of ‘something you have done (I’m mad at you, I don’t want to spend time with you, I would rather be with my friends/family)’ because it is (ridiculous, dumb, self-centered, unworkable) because of ‘some situation’. because I have a conflict because of prior history I am so sorry. Maybe another time I am just too busy for you. I am just too busy. I should make time for my good friends.

32 MATESMATES V.H. Allan: Utah State University 32 Judgment: You have such good ideas. You are so thoughtful to have asked. That does sound fun. You always want to do things I don’t like. You never consider my feelings. Why did you think I would want to do that?

33 MATESMATES V.H. Allan: Utah State University 33 Softening: You have so many good traits I’ve heard really good things about you I remember when we had a good time together.

34 MATESMATES V.H. Allan: Utah State University 34 Counter proposal: Maybe another time/day Maybe another activity Maybe we should do some other thing I know you like. Maybe we should do something we both like. Maybe we should do something only I like.

35 MATESMATES V.H. Allan: Utah State University 35 Grammar may be viewed as a stochastic finite state machine Rejection: reject+ explanation* judgment* soften? counter-proposal? reject Explanation Judgment soften counter reject

36 MATESMATES V.H. Allan: Utah State University 36 Thus, responses might vary from: No. Maybe another time I am so sorry. You have such good ideas. Maybe we should go bowling. Maybe we should go golfing. Maybe we should go tomorrow. Maybe we should go Friday. No. No. No. I won’t. I am too busy for you. You never consider my feelings.

37 MATESMATES V.H. Allan: Utah State University 37 Modeling Emotions Emotions are important in giving Disney characters the illusion of life. Believability vs realism: may be better to use simplified, exaggerated characters.

38 MATESMATES V.H. Allan: Utah State University 38 How to Combine Emotions Winner take all – ignore all but the highest intensity emotion Additive – but may be confusing to model joy and sadness simultaneously Logarithmic: log(2 emotion1 + 2 emotion2 ) Focus – kicking example

39 MATESMATES V.H. Allan: Utah State University 39 How created? Emotions are tied to goals (through personality survey). When a goal is achieved, attached emotions are generated. Factors: surprise, importance of goal, difference in emotion felt with success or failure of same goal. (e.g., goal: to have companion)

40 MATESMATES V.H. Allan: Utah State University 40 Goals Intensity Chance of succeeding Emotions generated when fail Emotions generated when chance of succeeding increases/decreases.

41 MATESMATES V.H. Allan: Utah State University 41 What kind of transformations? Decay – all at same rate? Combine Filter Idea: create an algebra of emotions through matrix manipulation

42 MATESMATES V.H. Allan: Utah State University 42 What effects emotions? Personality – each personality type will express emotions in its own way. relationships: affect what emotions are felt and how strongly memory: previous experiences (Were you angry when the first telemarketer called?)

43 MATESMATES V.H. Allan: Utah State University 43 Challenges Cardboard personalities? How create grammar? IPIP survey validated Gottman research well-respected, but is it valid for self-reporting? Goal data – unclear what goals to even ask about Held to a higher standard – not just entertaining. How do we test it? (subjective tests?)

44 MATESMATES V.H. Allan: Utah State University 44 After a quarrel, a husband said to his wife, “You know, I was a fool when I married you.” The wife replied, “Yes dear, but I was in love and didn’t notice.” Much testing is needed.


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