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 dissoci-ated 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 longi-

tudinally– 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 nega-

tive

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 Intelli-gent 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• 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 provid-ing 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 earn-

ing 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 behav-ior

– 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 as-

sesses 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