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Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
 

Depth of Feelings: Modeling Emotions in User Models and Agent Architectures

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Overview of alternative approaches to modeling emotions in user models and cognitive-affective agent architectures.

Overview of alternative approaches to modeling emotions in user models and cognitive-affective agent architectures.

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    Depth of Feelings: Modeling Emotions in User Models and Agent Architectures Depth of Feelings: Modeling Emotions in User Models and Agent Architectures Presentation Transcript

    • Depth of Feelings: Alternatives for Modeling Affect in User Models & Cognitive Architectures Eva Hudlicka Psychometrix Associates Blacksburg, US [email_address] psychometrixassociates.com TSD 2006 Masarykova Universita, Brno, Czech Republic September 15, 2006
    • “ Diseases of the Mind”* Are emotions….. *Immanuel Kant
    • “ reason is, and ought only to be the slave of the passions”
      • Hume, 1739
      Or are emotions essential for adaptive intelligent behavior…
    • Emotions in “Human” Interaction “ too little…”
    • Emotions in Human Interaction “ too much..”
    • Or Is There a Middle Ground?
    • Outline
      • Motivation & Objectives
      • Emotions – Background Info
      • Computational Models of Emotion
      • Framework for Model Analysis
      • Summary & Conclusions
    • Emotions in HCI: State-of-the-art KISMET - Cynthia Breazeal, MIT Media Lab
    • Emotions in HCI: State-of-the-art Agent Max - Becker-Asano et al.
    • Requirements for Affective HCI Affective User Model / Cognitive-Affective Architecture Emotion Sensing & Recognition “ Emotion” Expression OR? GRETA, Fiorella de Rosis, U. Bari
    • Why Include Emotions in User Models & Agent Architectures?
      • Emotion is a critical component of social interaction & individual motivation
      • Affective user models are more realistic, enabling:
        • Socially-appropriate dialogue (speech tone, content, turn taking)
        • More effective dialogue (persuasion, empathy)
          • Infer implied meaning & motivation
          • Predict affective reaction to system’s utterances
      • Affective agent architectures enable:
        • Socially-appropriate responses and behavior
        • May improve agent autonomy in complex, uncertain environments
      • Affect-adaptive user interfaces and responses
    • Outline
      • Motivation & Objectives
      • Emotions – Background Info
      • Computational Models of Emotion
      • Framework for Model Analysis
      • Summary & Conclusions
    • Definition(s) of Emotions
      • (See: roles & characteristics of emotions…)
      • Evaluative judgments of world, others, self …in light of agent’s goals and beliefs
    • Roles of Emotions Intrapsychic Interpersonal WHAT? * Social coordination * Rapid communication of behavioral intent; HOW? Express emotions via: -Facial expression -Speech (content & properties) -Gesture, Posture -Specific actions WHAT? * Motivation * Homeostasis * Adaptive behavior
      • HOW?
      • - Emotion generation (appraisal)
      • Emotion effects (processing biases)
      • Global interrupt system
      • Goal management
      • Prepare for coordinated actions
    • How Do We Recognize an Emotion if We See One?
      • Manifested across multiple , interacting modalities:
        • Somatic / Physiological (neuroendocrine - e.g., heart rate, GSR)
        • Cognitive / Interpretive (“Nothing is good or bad but thinking makes it so…”)
        • Behavioral / Motivational (action oriented, expressive, ‘visible’)
        • Experiential / Subjective (“that special feeling…”, consciousness)
      • Much terminological confusion can be attributed to a lack of consideration of these multiple modalities of emotions
        • e.g., Is emotion a feeling or a thought? - It’s both
    • Simple Fear “Signature”: Large, Approaching Object Increased heart-rate; Attacked? Crushed? Flee? Freeze? Feeling of fear Cognitive Subjective
    • A Taxonomy of Affective Factors Traits Affective Factors NOT ALL TRAITS are affective! Attitudes, Preferences… Affective States Emotions Moods Negative Positive Traits States “ Big 5” … Basic Anger Joy Fear … Complex Shame Guilt Pride …
    • Core Processes of Emotions Effects of Emotions (on cognition & behavior) Generation of Emotions (via cognitive appraisal) Cognitive-Affective Architecture Stimuli Situations Expectations Goals Cognitive Appraisal Emotions
    • Emotion Generation via Appraisal Stimuli Appraisal Dimensions Recalled Perceived Imagined Appraisal Process Emotions Existing emotions, moods, traits Goals (desires, values, standards) Beliefs, Expectations
    • Emotion Generation via Appraisal Stimuli Appraisal Dimensions Recalled Perceived Imagined Appraisal Process Emotions Existing emotions, moods, traits Goals (desires, values, standards) Beliefs, Expectations
        • Domain-Independent Appraisal Dimensions
        • Novelty
        • Valence
        • Goal / Need relevance
        • Goal congruence
        • Agency
        • Coping potential
        • Social and self norms and values
    • Emotion Effects on Cognition
      • Emotion and cognition function as closely-coupled information processing systems
      • Emotions influence fundamental processes mediating high-level cognition:
        • Attention speed and capacity
        • Working memory speed and capacity
        • Long-term memory recall and encoding
      • Influences on processing and contents
        • Transient biases influence processing
        • Long-term biases result in differences in long-term memory content & structure
    • Examples of Affective Biases
      • Anxiety
        • Narrows attentional focus
        • Reduces working memory capacity
        • Biases towards detection of threatening stimuli
        • Biases towards interpretation of ambiguous stimuli as threatening
        • Promotes self-focus
      • Positive emotions
        • Increase estimates of degree of control
        • Overestimate of likelihood of positive events
        • Promote creative problem-solving
        • Promotes ‘big picture’ thinking - focus on ‘the forest’
      • Biases can be adaptive or maladaptive, depending on context
    • “ Thank God! Those blasted crickets have finally stopped!”
    • Outline
      • Motivation & Objectives
      • Emotions – Background Info
      • Computational Models of Emotion
      • Framework for Model Analysis
      • Summary & Conclusions
    • Considerations Guiding Model Requirements
      • Why and when to model emotions?
      • Which emotions?
      • Which aspects of emotions?
      • Which affective processes?
      • Which theory?
      • What level of resolution? (Are the data available?)
      • Which architecture?
      • … Knowledge and data requirements
      • …… Representational and reasoning requirements
      • ……… ..Representational and reasoning formalisms and methods
    • Why and When to Model Emotions?
      • Research
        • Understand how emotions work in biological agents
      • Applied
        • More effective and ‘fun’ human-computer interaction
          • Decision-support
          • Training & tutoring
          • Recommender systems
          • Entertainment
        • More robust agent behavior
    • Why and When to Model Emotions?
      • Research
        • Understand how emotions work in biological agents
      • Applied
        • More effective and ‘fun’ human-computer interaction
          • Decision-support
          • Training & tutoring
          • Recommender systems
          • Entertainment
        • More robust agent behavior
      (Breazeal, 2003) (de Rosis, 2003)
    • Which Emotions and Affective Factors to Model?
      • Model objectives & application influence selection:
        • User models for decision-support systems in stressful settings: fear, anxiety, frustration, surprise, boredom – probably not pride, shame, guilt
        • Synthetic agents for children: happiness, sadness, fear, anger …also pride, shame
        • User models & agents in training and tutoring: happiness, fear/anxiety, frustration, surprise, boredom
    • A Taxonomy of Affective Factors States Affective States Emotions Moods Basic Complex Negative Positive Anger Joy Fear Shame Guilt Pride Traits Traits Affective Factors “ Big 5” …
    • But Exactly Which Aspects of Emotions Should We Model?
      • Recall the multiple modalities of emotions:
        • Somatic / Physiological (infrequent)
        • Cognitive / Interpretive (most frequent)
        • Behavioral / Motivational (most frequent)
        • Experiential / Subjective (infeasible?)
      Cognitive Subjective
    • Emotion Roles Emotion Generation Emotion Effects on Cognition & Behavior Which Processes to Model?
      • Social
      • Communication - Coordination
      • … .
      Intrapsychic: - Goal management - Behavior preparation -…… implement
    • Computational Tasks for Appraisal Models Stimuli
      • Emotion attributes:
      • Complexity of emotion construct
      • * type
      • * intensity
      • * cause …
      • * direction
      • * …
      • Types of stimuli:
      • Internal / External
      • Real / Imagined
      • Past / Present / Future
      • Domain specific / Abstract appraisal dimensions
      • Complexity of stimulus structure
      • Mental constructs required (e.g., goals, expectations)
      • Stimuli-to-emotion mappings
      • Intensity calculation
      • Nature of mapping process:
      • * Stages & functions
      • * Degree of variability
      • Integrating multiple emotions
      • Emotion dynamics over time
      Emotions
    • Most Influential Appraisal Theories in Computational Models
      • Ortony, Clore and Collins (OCC) (1988)
      • Leventhal and Scherer --> Scherer (1984 …)
      • Arnold  Lazarus  Smith and Kirby (1960 …)
    • Example #1: OCC Appraisal Model
    • Valenced Reactions Event-based emotions Attribution emotions Attraction emotions Event Related Appraised wrt goals “ Does this promote world peace?” Acts-by-Agents Related Appraised wrt standards “ Was it appropriate for John to rob the bank?” Object Related Appraised wrt attitudes “ Is this appealing to me?”
    • Valenced Reactions Event-based emotions happy for, pity, gloating.. joy,distress hope, fear gratitude, anger desirability (pleased / displeased) desirability for other (deserving, liking) likelihood praiseworthiness (approve / disapprove) appealingness (like / dislike) degree of autonomy, expectation deviation familiarity Attribution emotions Attraction emotions Fortunes-of-self emotions Fortunes-of-others emotions Prospect-based emotions Well-being emotions pride, shame, reproach love,hate
    • Valenced Reactions Event-based emotions Attribution emotions Attraction emotions Fortunes-of-self emotions Fortunes-of-others emotions happy for, pity, gloating.. distress Prospect-based emotions Well-being emotions anger reproach love,hate Desirability = low fear Praiseworthiness = low degree of autonomy = high expectation deviation = high
    • Example #1: OCC Appraisal Model
      • Developed to provide a “computationally tractable model of emotion”
      • Taxonomy of triggering conditions and emotion types
      • Specification of variables affecting intensity
        • “ Global” (physiological state…)
        • “ Local” (appraisal dimensions…)
      • Many implementations (Elliot, Reilly, Bartneck, Andre, Gratch…)
    • Example #2: Scherer‘s “Component Process Model”
    • Coping potential Norms Relevance Appraisal variables Novelty Valence Goal relevance Certainty Urgency Goal congruence Agency Stimuli Implications Coping Norms Emotion
    • STIMULI Novelty Valence Goal relevance Outcome probability Urgency Goal congruence Agency Coping potential Norms high high v. high low other low low high FEAR
    • Example #2: Scherer‘s “Component Process Model”
      • Emphasis on domain-independent appraisal dimensions (emotion components)
        • Emotions defined as patterns of appraisal variable values
        • Variables evaluated in a fixed sequence
      • Appraisal as a dynamic, evolving process
      • … across multiple modalities
      • … at multiple levels of complexity
        • Conceptual
        • Schematic
        • Perceptual-motor
      • Implementations:
        • Black-box implementations
        • Appraisal dimensions adopted in cognitive-affective architectures
    • Results of the Appraisal Process: Emotion ‘Specification’ fear .90 probability, importance of affected goals 2 minutes (exp. decay) { aggressive dog | owner} “ aggressive dog approaching” negative { dog | negligent owner | self } low { safety of self | safety of dog | delay } Other appraisal variables….: Type: Descriptive detail: Intensity: Variables affecting intensity: Cause: Direction: Coping potential: Duration: Valence: Goals affected:
    • Representation & Reasoning Alternatives
      • Vector spaces (Scherer)
      • Connectionist (Velasquez)
      • Symbolic
        • Rules (Marinier, Jones, Henninger, Hudlicka…)
        • Belief nets (de Rosis, Hudlicka, …)
      • Complex symbolic structures (Elliot, Reilly, Gratch & Marsella)
        • Appraisal frames, causal plan structures
      • Spreading activation over networks of processes (Breazeal)
      • Decision-theoretic
        • Decision trees
        • Decision theoretic formulations (Gratch & Marsella, Lisetti & Gmytrasiewicz)
      • Blackboards and ‘specialists’ (Gratch & Marsella)
      • Finite state machines (Kopecek)
      • Markov models (El Nasr)
      • Theorem proving (Zippora)
      • Dynamical systems
    • Bayesian Belief Networks (MAMID, Hudlicka)
    • Complex Causal Interpretation (EMA, Gratch & Marsella)
    • Emotion Effects on Cognition Cognitive-Affective Architecture Stimuli Situations Expectations Goals Affect Appraiser Emotions
    • Computational Tasks for Modeling Emotion Effects Emotion(s)
      • Cognition Attention, perception, memory,
      • learning, problem-solving, decision-making…)
      • Behavior Verbal, non-verbal, action selection
      • … & other affective factors:
      • Affective States
      • Moods
      • Traits
      • Processes and structures affected
      • Variability in effects (by intensity, by individual…)
      • Integration of multiple emotions (in cognition, in behavior)
      Effect(s)
    • Influential Theories
      • Fewer theories exist than for appraisal
      • Specific mechanisms of emotion effects not as well developed
      • Existing theories:
        • Spreading activation & priming (Bower, 1984)
          • … memory effects & biases
        • Distinct modes of processing associated with different emotions (Oatley & Johnson-Laird, 1987)
        • Emotions as patterns of parameters modulating processing
          • (Fellous, Hudlicka, Matthews, Ortony et al., …)
    • Emotions As Distinct Modes of Processing
      • Parameter-controlled ‘global’ effects across multiple processes
        • Neuromodulation theories (Fellous, 2004)
      • Effects on low-level fundamental processes: attention & working memory
        • Speed & capacity & content bias (e.g., threat, self)
      • Effects on long-term memory
        • Encoding and retrieval: speed & elaboration & bias (threat, self)
      • Effects on higher-level processes
        • Problem-solving, decision-making, planning..
        • Affect appraiser processes (e.g., assessments of coping potential)
      • Can ‘higher-level’ effects be explained (& implemented ) in terms of effects on the fundamental processes?
    • Emotions As Parameters (MAMID, Hudlicka) Traits Extraversion Stability Conscientiousness Aggressiveness STATES / TRAITS Processing Structural Module Parameters Construct parameters Architecture topology Long-term memory speed, capacity Cue selection & delay …. Data flow among modules Content & structure Affective States Anxiety Anger Sadness Joy ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Attention Action Selection Situation Assessment Goal Manager Expectation Generator Affect Appraiser
    • Modeling Threat Bias Processing Parameters Construct parms. - Cue selection - Interpretive biases ... Process Threat cues Process Threatening interpretations Traits Low Stability TRAITS / STATES COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Attention Action Selection Situation Assessment Goal Manager Expectation Generator Affect Appraiser Emotions Higher Anxiety / Fear Predisposes towards Preferential processing of Threatening stimuli Threat constructs Rated more highly
    • Modeling Affect-Induced Differences in Behavior
      • MAMID architecture modeled behavior of peacekeeper unit leaders
      • Units encountered a series of ‘surprise’ events en route
        • Hostile crowds
        • Ambushes
        • Destroyed bridges
      • Different leaders defined by distinct personality profiles:
        • “ Normal” leader
        • “ High anxious” leader
        • “ High aggressive” leader
      • Parameter-controlled ‘micro effects’ resulted in observable differences in behavior & distinct ‘mission outcomes’
    • Distinct Individual Profiles & Behavior “ Normal” “Anxious” Attention Perception / Situation Assessment Expectation Generation Affect Appraisal Goal Selection Action Selection Hostile large crowd Hostile large crowd Objective near Unit capability high Limited # of high-threat & self cues Movement blocked Danger to unit low Danger to unit and self high Perceptual threat & self bias Anxiety: Normal Anxiety: High Rapid-onset of high anxiety Danger from crowd unlikely Danger to unit and self high Career success threatened Threat and self oriented expectations Non-lethal crowd control Reduce anxiety Defend unit Threat and self focus goals Stop Stop; Lethal crowd control Non-lethal crowd control Report info Request help Request info Anxiety regulating behavior
    • Representation & Reasoning Alternatives
      • Symbolic - specific emotions linked to particular effects & behavior
        • Rules
        • Belief nets
      • Connectionist (Araujo)
        • Parameters bias processing within a network
      • Decision-theoretic (Busemeyer)
        • Decision field theory
    • What Level of Resolution? “Black box” vs. “Process” models
      • May be all that is required for a particular application
      • Easier to build (…initially)
      Black box models - simulate input-output mappings ??? INPUT OUTPUT Don’t know and don’t care Stimulus Emotion Emotion Effects
    • Process Models - emulate internal processing
      • Implement hypothesized mechanisms mediating the I-O mapping
      • Necessary if aiming to understand emotion processes
      • More difficult initially, but more robust and general
      Cognitive-Affective Architecture INPUT OUTPUT Process #1 Process #2 Process #3 Memory A Memory B Would like to know and do care
    • What Type of an Architecture?
      • Which architectural components are necessary?
        • Attention, situation assessment, expectation generation, affect appraiser, planner..?
        • Data and control paths among the modules?
      • What fundamental processing paradigm?
        • Sequential see-think-do (see-think/ feel- do?)
        • vs. parallel distributed processing
      • Where does emotion reside within the architecture?
        • Emotion as dedicated modules?
        • vs. emotions as modulating parameters?
        • vs. emotions as emergent properties of a complex, multi-level architecture?
    • Components of a Cognitive-Affective Architecture : See-Think-Feel-Do
      • “ See”
        • Attention
        • Sensing and Perception
      • “ Think”
        • Situation Assessment
          • Causes
          • Current assessments
          • Future predictions (expectations)
        • Goal management
          • Goals, drives, desires, norms, value
        • Problem solving, Planning, Learning
        • Memory (declarative, procedural, episodic) (sensory, working , long-term)
      • “ Feel”
        • Affect appraiser
        • Emotion effects
      • “ Do”
        • Effectors
        • Performance monitoring
    • Questions Regarding Representational & Reasoning Requirements
      • What must represented explicitly?
        • Time (present, past, future)
          • Hope needs expectations, regret needs past
        • Mental constructs
          • situations, expectations, goals
        • Memories
          • what type – declarative, episodic, procedural
        • Explicit representation of the self
          • need for complex emotions, social interaction, coping
      • What types of reasoning are necessary?
        • What-if
          • … to generate expectations which influence emotions
        • Causal explanation
          • ..important for attribution
    • Examples of Cognitive-Affective Architectures
      • Emotion-augmented cognitive architectures
        • Recognition-primed decision-making (Hudlicka)
        • Belief-Desire-Intention architectures (de Rosis…)
        • Soar ( Marinier, Jones, Henninger )
        • ACT ( Ritter )
      • Generic - the ‘triune’ architectures
        • Sloman et al., (Cog_Aff) or Sim_Agent (implementation)
        • Leventhal & Scherer (design)
        • Ortony, Normal and Revelle (ONR) (design)
    • MAMID Cognitive-Affective Architecture Action Selection Cues: State of the world ( “growling dog”, “approaching”) Situations: Perceived state ( “aggressive dog” ) Expectations: Expected state (“dog will attack”, “bite wound”) Goals: Desired state (“protect self”) Actions: to accomplish goals (“climb tree”) Affective state & emotions: Negative valence High anxiety Low happiness Cues Actions Attention Situation Assessment Expectation Generator Affect Appraiser Goal Manager
    • “ The Triune” Generic Architectures (Sloman; Leventhal & Scherer; Ortony et al.; Arbib & Fellous..) Reactive Routine Reflective hardwired, fixed Well-learned behavior Awareness Compare alternatives - detect deviations Simple ‘what if’ Symbolic processing Approach / Avoid Simple drives Complex mental models Self representations & self-awareness Explicit predictions, causality… Meta-cognition Proto-affect Good/bad Primitive Emotions Good/bad Now/later Full fledged emotions -Basic -Complex flexible
    • Outline
      • Motivation & Objectives
      • Emotions – Background Info
      • Developing Affective User Models
      • Framework for model analysis
      • Summary & Conclusions
    • Framework for Development, Analysis and Comparison of Models
      • Which modeling objectives?
      • Which emotions?
      • Which aspects of emotions? (modality, functions, roles)
      • Which processes modeled? (appraisal, effects)
      • Which theory is basis of model?
      • What degree of model resolution
      • Which architecture? (modules, processes, data & control flow)
      • Which representational & reasoning formalisms used?
      • What validation method used?
    • Outline
      • Motivation & Objectives
      • Emotions – Background Info
      • Developing Affective User Models
      • Framework for model analysis
      • Summary & Conclusions
    • Summary
      • Need for including (some) emotions in (some) user models
      • Background info from emotion research in psychology and neuroscience
      • Guidelines for development of affective user models & cognitive-affective architectures
        • Requirements for modeling core components of emotions:
          • Cognitive appraisal
          • Emotion effects and emotion-cognition interactions
      • Framework for analysis of computational models of emotion
    • Successes & State of the Art (1)
      • Research
        • Terminological clarifications
        • Increasing interaction among experimentalists & modelers and theorists
        • Construction of process models of appraisal theories
        • Beginnings of process models of emotion effects
        • Convergence on architecture structure
        • Beginnings of principled analyses of modeling requirements
    • Successes & State of the Art (2)
      • Research
        • Terminological clarifications
        • Increasing interaction among experimentalists & modelers and theorists
        • Construction of process models of appraisal theories
        • Beginnings of process models of emotion effects
        • Convergence on architecture structure
        • Beginnings of principled analyses of modeling requirements
      • Applications
        • Many ‘shallow’ models enhancing HCI and agents
        • Beginnings of ‘deep’ models driving synthetic agent & robot behavior
        • Emotion sensing & recognition
        • Emotion “expression”
      Gratch & Marsella De Rosis Breazeal
    • Challenges
      • Theories to guide model building
        • Appraisal, mechanisms of emotion effects, meta-cognition & emotion
        • Emotion dynamics
          • Multiple emotions & non-linear effects
          • Interaction among multiple modalities
      • Data
        • Emotion experiments are difficult
        • Model data requirements frequently exceed data availability
      • Model development
        • Standards, shared data & ontologies, plug & play modules, guidelines
        • Can we build LTM’s or must they “evolve” through agent-environment interactions (Matthews, 2004)
      • Validation
        • Verification vs. validation
        • Developing validation criteria & benchmark problems
    • Parting Thought
      • “ Anyone can model emotions. That is easy.
      • But to model emotions
      • - in the right context,
      • - to the right degree,
      • - at the right time,
      • - for the right reason, and
      • - in the right way,
      • this is not easy.”
      • Paraphrasing “On anger”, Aristotle, Nichomachean Ethics
    • Depth of Feelings: Alternatives for Modeling Affect in User Models & Cognitive Architectures Eva Hudlicka Psychometrix Associates Blacksburg, US [email_address] psychometrixassociates.com TSD 2006 Masarykova Universita, Brno, Czech Republic September 15, 2006