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Matthias Scheutz Articial Intelligence and Robotics Laboratory Department of Computer Science and Engineering University of Notre Dame, Notre Dame, IN 46556, USA [email protected]. Useful Roles of Emotions in Artificial Agents: A Case Study from Artificial Life. - PowerPoint PPT Presentation
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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, [email protected]
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