Useful Roles of Emotions in Artificial Agents: A Case Study from Artificial Life

Matthias ScheutzArticial Intelligence and Robotics LaboratoryDepartment of Computer Science and EngineeringUniversity of Notre Dame, Notre Dame, IN 46556, USAmscheutz@cse.nd.edu

In Proceedings of AAAI 2004 , AAAI Press

Lior Shamir

Applications

• Human/Robot Interaction

• User Interface Design

• Natural Language Processing

• Agent Modelling

•Video Games & Entertainment

External Environment AgentEmotions

input

action

Roles For Emotions In Artificial AgentsRoles For Emotions In Artificial Agents action selection (e.g., what to do next based on the current emotional state) adaptation (e.g., short or long-term changes in behavior due to the emotional states) social regulation (e.g., communicating or exchanging information with others via emotional expressions) sensory integration (e.g., emotional filtering of data or blocking of integration) alarm mechanisms (e.g., fast re ex-like reactions in critical situations that interrupt other processes) motivation (e.g., creating motives as part of an emotional coping mechanism) goal management (e.g., creation of new goals or reprioritization of existing ones) learning (e.g., emotional evaluations as Q-values in reinforcement learning) attentional focus (e.g., selection of data to be processed based on emotional evaluation) memory control (e.g., emotional bias on memory access and retrieval as well as decay rate of memory items) strategic processing (e.g., selection of dierent search strategies based on overall emotional state) self model (e.g., emotions as representations of what a situation is like for the agent)

AIBOAn Ethological and Emotional Basis for Human-Robot InteractionRonald C. Arkin*, Masahiro Fujita**, Tsuyoshi Takagi**, Rika Hasegawa**

3 ObjectsWATERFOODMASTER

6 EmotionsHungerThirstEliminationTirednessCuriosityAffection

ParleE: An Adaptive Plan Based Event Appraisal Model of Emotions

Duy Bui, Dirk Heylen, Mannes Poel, and Anton Nijholt

Emotion impulse at time t

Emotion at time t-1

Emotion attime t

Decay Function

Event

EIV(Emotion Impulse Vector)

ESV(Emotion State Vector)

Impact(event,goal) = Pafter(goal) - Pbefore(goal)

Hope

Hope = all goal Import(goal) × (P(goal) - 0.5)

Fear

Fear = all goal Import(goal) × (0.5 - P(goal))

Happiness

Sadness

all goal Impact(event,goal) × Import(goal) × (1 - P(event))1/2

all goal Impact(event,goal) × Import(goal) × (1 - P(event))2

Desirability(event) = all goal Import(goal) × Impact(event,goal)

LikingLevel t+1=max(1.0, min(1.0,LikingLevel t+0.1* Desirability(event)))

Liking

Happy-For

Happy for = LikingLevel(another agent) × (another agent’s happiness)

Obie

FLAME - Fuzzy Logic Adaptive Model of Emotions

Magy Seif El-Nasr, John Yen, Thomas R. Ioerger

Desirability of events

Degree of importance

Degree of impact:

IF Impact(G1,E) is A1AND Impact(G2,E) is A2…..AND Impact(Gk,E) is AkAND Importance(G1) is B1AND Importance(G2) is B2….AND Importance(Gk) is Bk

THEN Desirability(E) is C

Example:IF Impact(prevent starvation, food dish taken away) is HighlyNegativeAND Importance(prevent starvation) is ExtremelyImportantTHEN Desirability(food dish taken away) is HighlyUndesired

Hope (1.7 * expectation 0.5 ) + (- 0.7 * desirability)

Hope

Useful Roles of Emotions in Artificial Agents: A Case Study from Artificial Life

1800 x 1800 grid.

Initially 50 resources. Each cycle adds another resource.

Each resource = 800 energy points.

Speed is between 1 and 4.

Movement cost = distancespeed2

Below 400 energy units – speed = 1

Agents compete for resources and can fight or flee. Fighting cost 50 units. Fleeing is in the speed of 7 for 5 to 10 cycles.

Probability of fighting is public for each agent.

Rules of the GameRules of the Game

D = Gr*resource(n) + Ga*agent(m)

resource(n) – Vector from the position of A to the Nth resourceagent(m) – Vector from the position of A to the Mth agent

Social Agents

Vs. Asocial Agents

Fight only if their tendency is higher than the oponents

tendency

Based only on their own tendency

Adaptive Agents

Vs. Non-Adaptive

Agents

Lower their action tendenacy if they win.

Don’t change action tendency based on the results

Ar(m)+ = { m + (1-m)/2 m>=r2m m<=r/2r + (2m –r)*(1 – r)/2r r/2<m<r

Ar(m)- = { m - (1-m) m>=r + (1-r)/2m/2 m<=rr/2 + r*(m –r)*(1 – r) r<m<r + (1-r)/2

r – basic action tendencym – current action tendency

Conflict Tendancy Action Tendency Emotional State

> 0 > 0.5 angry < 0 < 0.5 fearful

Emotional Agents

ga = action_tendency*100 - 50

Asocial Emotional

Asocial Non-Adaptive

Social Non-Adaptive

Asocial Adaptive

Social Adaptive