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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