Emotional agent
: A modeling and an application
Khulood et al.
Information and Software Technology
vol. 49, pp. 695-716, 2007
장수형
Introduction
• Emotion
– Essential part of human
– Influence how we adapt, learn, behavior, communicate with others
– Important and active role in the human decision-making process
– Use artificial agent as test bed
– Exploit some of the roles that emotions play in biological system
• Develop mechanisms and tools to ground and enhance autonomy
• Definition of emotion
– Occurring when the cognitive, physiological, and motor/expressive
components are usually more or less dissociated in serving
separate functions
– Psychological states
Introduction
• The different affective states
– Emotion
• Angry, sad, joyful, fearful, ashamed, proud, elated, desperate
– Mood
• Cheerful, gloomy, irritable, listless, depressed, buoyant
– Preferences/Attitudes
• Liking, loving, hating, valuing, desiring
• Emotion History
– Psychology, Neurology, Philosophy, Cognitive science
– ‘The Emotional Brain’ – LeDoux
• Emotional process in the brain
– Terms of desires and expectations
Emotion
• Role of emotion in nature
– Serve several crucial roles in animals and human alike
– Provide a basic evaluation in terms of hedonic values
– Cause the organism to be attracted to what it likes and to avoid
what it does not like
• Fear-anger system may generate fight or flight behavior
– Influence direct cognitive process, process strategies
– Play an important role in social contexts
• Raging from signaling emotional state through facial expressions and gesture
Emotion
• Role of emotions in artificial agents
– Action selection
– Adaptation
– Social regulation
– Sensory integration
– Alarm mechanisms
– Motivation
– Goal management
– Learning
– Attention focus
– Memory control
– Strategic processing
– Self-model
Emotion
• Emotion cognitive appraisal
– Complex , dynamic, varying both episodically and longitudinally
– A negative event can trigger an emotional response
• Dissipate within a short time
Emotion
• OCC-model
– Vague, vary, difficult to tell apart
• A lot of confusion
– Basic emotions, reduce everything
– Consider the best categorization of emotion
– World as divided in three different categories
• Events, agents, objects
– Categories down to five distinct positive and five negative
Emotional agent modeling
• Symbolic approach one
• Model the behavior of two agents and objects living in a
simulated world
• RIA(Regular Intelligent Agent), EIA(Emotional Intelligent Agent)
– Same object
– Main goal to achieve certain activities
• Agent’s global variables and states can be monitored through
the simulation using graph, plot, report
Emotional agent modeling
• Model hypothesis
– The mission ‘To bring life’ to several application
• Information, transaction, education, tutoring, business, entertainment
and e-commerce
– Develop artificial mechanisms
• Can play the role emotion plays in natural life
• Artificial emotions
– NetLogo
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Extensions of the LOGO
Control many agent on the screen
http://ccl.northwestern.edu/netlogo.
An agent modeling environment
Well suited for modeling complex system
Hundreds or thousands of independent ‘agents’
Netlogo
• System
– Run on MacOS, Windows, Linux, et al.
– Model can be saved as applets to be embedded in web
• Language
– Fully programmable
– Simple language structure
– Language is Logo
– Unlimited number of agent and variable
– Integer and double precision floating-point math
– Runs are exactly reproducible cross platform
• Environment
– Graphics display supports turtle shapes and size, exact turtle
positions, and turtle and patch label
– Interface builder with buttons, sliders, switches, choices, monitors,
and text boxes
Orphanage scenario
• Require the develop of a Netlogo environment providing a set
of behavior rules
– Be used for the simulation of agent behavior
– Emotional agent behavior toolkit
• The Orphanage Care Problem
– Two agents
• To achieve agent’s main goal
– Agent should go to Orphanage
– Taking care of the Orphanage depends on the agent working
capacity
– Taking care of the Orphanage depends on the agent earning level
Orphanage scenario
• To preserve earning level from decline to zero
– Agent should go to work to make some money
• Working capacity can be improved at the Academy
– Agent should go to the Academy
• Improve its knowledge, hence, earning salary
– Agent must pay fee
– Working capacity do not decay over time
• Agent need to raise its social capacity
– Agent should go a social place such as club, restaurant, mall, party
– Going their need expense which costs money
– Social capacity do not decay over time
Emotion on Orphanage
• Important roles at the control-level of agents behavior
– Lead a reflexive reaction
– Support the goal and motivation of an agent
– Can create new motivations
– Operate a the lever of control of agent architecture
– Behavior of the agent will improve
– Agent can generate emotion signal, evaluate and assesses events
• Integration of Agent Goal, Personality, behavior
– Netlogo can be varying initial conditions
Agent attributes
Agent attributes
• Agent attributes
Simulation of objects
• The Orphanage
• Job
• Club
• Academy
• Main-Goal Status
• Agent’s Performance Measure
– Social capacity
– Working capacity
– Earning level
• Make sure
– Their orphanage status does not decay completely
– They do not run out of money
General Concept
Agent goals
• Main goal
– Take care of the Orphanage and as good as they can
• Sub goal
Emotions in the simulation model
• Emotions come into play just after event perception
– Compare to its goals and standards
– Attitudes are also…
– Result
• Value of Event-based emotion, attribution emotions and attraction emotions
– The appraisal/evaluation mechanism
• OCC-theory
– Rise to emotions
• Less complex than the full human emotion spectrum
• Play a meaningful role in the mental processes
Emotions in the simulation model
• Emotion parameters
– Event-based emotions
• Be influenced by the level of Orphanage-Status, earning level, social, work capacity
– Attribution emotions
• Be affected by the measure of other agents(work, learn or socialize with)
– Attraction emotions
• Works the same as attribution emotion
• With liking/disliking of object
• Agent behavior
– ‘Behaviors with perception’ concept
• Depend upon the current state of environment(perception)
• State of the world, other agent
RIA
• Thinking process(RIA)
– Receives its initial states of it memory, parameter
– Perceive behavioral Environment
– Goal filtering
– Behavior/action
• Perception(behavior environment scanning)
– Scans the environment and gathers data about object features
– Assign priorities to the goal action
• The orphanage status level
• The earning level
– Perception rules
• Checks money level and give certain priority for job
• Reasoning(simple inference)
– If there are two goal-actions with equal priority
– Decide which action is important
• Execution
– With highest priority
• Going-to-job, learning-at-academy, socialize-at-clue
– Result can be noticed
• Orphanage status level
• Working/social capacity
• Earning level
EIA
• Thinking process(EIA)
– Receive its initial states of its memory and other initial parameter
– Perceive the behavioral environment
– Appraisal for situation is performed
– Emotion generation
– Emotion normalization
– Personality influence
– Goal Filtering
– Action
EIA
• Evaluate agent’s attitudes
EIA
Simulation
• Test
– 2000 iteration of the simulation
Result
• Run 10 times
Result
Result
conclusions
• Artificial emotions can be used in different ways to influence
decision-making
• Orphanage Care Problem
• Emotions can be successfully modeled in agent
• Outperform it non-emotional counterpart
• EIA can be used in decision making in dynamic system model
E.N.D