1. Agents to Simulate Social Human Behaviour in a Work Team Agents to Simulate Social Human Behaviour in a Work Team Barcelona, February 2003. Arantza Aldea aaldea@etse.urv.es S PAIN

2. Research Activities n Applications of AI to industry –Multi-Agent Systems –Ontology-Based Search & Information Extraction –Model-Based Reasoning

3. Multi-Agent Systems n Information Society –AgentCities n Simulation of work teams

4. Agents to simulate social behaviour  Introduction: Team Configuration  The Model:  General Architecture  Internal Architecture of the Agents.  Tasks Representation  Organisation Level  The Prototype  Future Work  Conclusions

5. Introduction In any complex project that requires several people working together, the configuration of the best possible team within the working constraints, is a difficult task (Biegler et al. 1997). Intelligent Multi-Agent Systems can be used to simulate the human behaviour:  Emotions and Personality  Sociability  Groups Organisation Case Study: selection of people to integrate a team in charge of the conceptual design of a chemical process. Brief Description

6. The Model: General Architecture The Team Simulation Process.

7. Social Status Cognition Perception BehaviourActor Basic Architecture of PECS Agents. (Urban 2000) Sensor EmotionPhysics The Model: Agents Architecture

8. Social Status Cognition Perception BehaviourActor PECS Modified Architecture. Emotion / Personality The Model: Agents Architecture

9. Cognition.  Project Manager  Chemical Engineer  Technician  Assistant  Creativity  Experience Social Status.  Introverted / Extroverted  Prefers to work in team / Prefers to work alone Sociologists, psychologists. Internal Parameters of the Agents } Emotion / Personality.  Desire } Frijda 1986  Interest  Disgust  Anxiety } Johnson-Laird 1992  Stress  Amiable  Expressive  Analytical  Driver S. Schubert, Leadership Connections Inc. 1997 } Izard 1991 }

10. Personality Trends Amiable. Priority: Relationships Speciality: Support; “We’re all in this together so let’s work as a team” Expressive. Priority: Relationships Speciality: Socialising; “Let me tell what happened to me...” Analytical. Priority: The task Speciality: Processes; systems; “Can you provide documentation for your claims?” Driver. Priority: The task Speciality: Being in control; “I want it done right and I want it done now.”

11. BEGIN TASK 1TASK 2 TASK 3 TASK 4 TASK 5 TASK 6 TASK 7 TASK 8... TASK N END  Number of Participants  Duration  Sequence  Difficulty of the Task  Type of Task  Deadline  Priority  Cost  Quality The Model: Tasks Representation

12. Differents organisation types will be tested: Tree Hierarchy Centralised Hierarchy Without Hierarchies The Model: Organisation Level

13. The Model: Agent Behaviour ms The agent behaviour emerges by evaluating values of its internal properties, randomly modified around the initial values of each of the properties. Normal distribution with mean m (internal value of the agent, e.g. stress, interest, etc.) and standard deviation s These random variations are due to the non-deterministic nature of human behaviour

14. Three Dimensions to Evaluate a Team:  Number of Members.  Agents with Different Characteristics.  Type of Organisations. The Model: Team Evaluation

15. Configuration Component  Team Configuration  Tasks Configuration Simulation Component  Selection of Weight Agent Internal Parameters  Number of Simulations Results Component  Show Team Behaviour  Graphical Statistics (future work) The Prototype: Main Components

16. The Prototype: User Interfaces Tasks Configuration Selection of Weight Agent Internal Parameters Use of JADE 2.5

17. The Prototype: Initial Results Team Members  1 Project Manager  3 Engineers  3 Technicians  3 Assistants Example Configuration: Project  12 Different tasks Number of simulations  100 Organisation  Centralised Hierarchy

18. The Prototype: Initial Results  Agents with high stress and in charge of a specialised task were the agents with more failures  Decreasing the stress parameter and executing the same number of simulations, the number of failures also decreased  Agents with an amiable personality and working in a task by themselves had more failures than successes  The most successful agents contains a high experience and analytical personality.  The creativity parameter only increases the number of successes when the agent is in charge of specialised tasks. We observe that:

19.  We are evaluating Fuzzy Logic to improve the agents behaviour.  More studies at organisation level: social characteristics, co- ordination, re-organisation, etc.  Implementation of other types of team co-ordination: tree hierarchical organisation, and no hierarchy  Validate the experimental results by comparing them with human teams  Unexpected events: how external factors affect the team behaviour Future Work Things to do...

20. At the end... Conclusions Emotions, Personality, Stress Different Organisation Types Social Characteristics Simulation Process... help the project manager to select the most suitable team members for a specific project.