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

1. Matthias Scheutz Articial Intelligence and Robotics Laboratory Department of Computer Science and Engineering University of Notre Dame, Notre Dame, IN 46556, USA mscheutz@cse.nd.edu Useful Roles of Emotions in Artificial Agents: A Case Study from Artificial Life In Proceedings of AAAI 2004 , AAAI Press Lior Shamir

2. Applications • Human/Robot Interaction • User Interface Design • Natural Language Processing • Agent Modelling • Video Games & Entertainment

4. External Environment Agent input Emotions action

5. Roles 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)

6. AIBO An Ethological and Emotional Basis for Human-Robot Interaction Ronald C. Arkin*, Masahiro Fujita**, Tsuyoshi Takagi**, Rika Hasegawa** 3 Objects WATER FOOD MASTER 6 Emotions Hunger Thirst Elimination Tiredness Curiosity Affection

8. ParleE: An Adaptive Plan Based Event Appraisal Modelof Emotions Duy Bui, Dirk Heylen, Mannes Poel, and Anton Nijholt Emotion impulse at timet Emotion at time t Emotion at time t-1 Decay Function

9. Event EIV (Emotion Impulse Vector) ESV (Emotion State Vector)

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

11. Hope Hope =Sall goal Import(goal) × (P(goal) - 0.5) Fear Fear = Sall goal Import(goal) × (0.5 - P(goal))

12. Happiness Sall goal Impact(event,goal) × Import(goal) × (1 - P(event))1/2 Sadness Sall goal Impact(event,goal) × Import(goal) × (1 - P(event))2

13. Liking Desirability(event) = Sall goal Import(goal) × Impact(event,goal) LikingLevel t+1=max(1.0, min(1.0,LikingLevel t+0.1* Desirability(event)))

14. Happy-For Happy for = LikingLevel(another agent) × (another agent’s happiness)

15. Obie

16. FLAME - Fuzzy Logic Adaptive Model of Emotions Magy Seif El-Nasr, John Yen, Thomas R. Ioerger Desirability of events

17. Degree of importance

18. Degree of impact:

19. IF Impact(G1,E) is A1 AND Impact(G2,E) is A2 ….. AND Impact(Gk,E) is Ak AND Importance(G1) is B1 AND Importance(G2) is B2 …. AND Importance(Gk) is Bk THEN Desirability(E) is C Example: IF Impact(prevent starvation, food dish taken away) is HighlyNegative AND Importance(prevent starvation) is ExtremelyImportant THEN Desirability(food dish taken away) is HighlyUndesired

20. Hope Hope = (1.7 * expectation0.5 ) + (- 0.7 * desirability)

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

22. Rules of the Game 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 = distance·speed2 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.

23. D = S Gr*resource(n) + S Ga*agent(m) resource(n) – Vector from the position of A to the Nth resource agent(m) – Vector from the position of A to the Mth agent

24. Social Agents Asocial Agents Vs. Fight only if their tendency is higher than the oponents tendency Based only on their own tendency

25. Adaptive Agents Non-Adaptive Agents Vs. Lower their action tendenacy if they win. Don’t change action tendency based on the results

26. r – basic action tendency m – current action tendency { m + (1-m)/2 m>=r 2m m<=r/2 r + (2m –r)*(1 – r)/2r r/2<m<r Ar(m)+ = { m - (1-m) m>=r + (1-r)/2 m/2 m<=r r/2 + r*(m –r)*(1 – r) r<m<r + (1-r)/2 Ar(m)- =

27. Emotional Agents ga = action_tendency*100 - 50 Conflict Tendancy Action Tendency Emotional State > 0 > 0.5 angry < 0 < 0.5 fearful

28. Asocial Emotional Asocial Non-Adaptive Social Non-Adaptive Asocial Adaptive Social Adaptive