Ivica Mitrovic and Kerstin Dautenhahn (2003)
Social Attitudes: Investigations with Agent Simulations Using Webots
Journal of Artificial Societies and Social Simulation
vol. 6, no. 4
To cite articles published in the Journal of Artificial Societies and Social Simulation, please reference the above information and include paragraph numbers if necessary
Received: 9-Jan-2003 Accepted: 8-Jul-2003 Published: 31-Oct-2003
|Table 1. Socio-political attitudes, of three ideal types, toward issues that are selected from their stories (grand narratives), as presented in Petric et al. (2002)|
|Figure 1. Examples of three different agents: agent with indeterminate socio-political type (neo-liberal component is 0, alternative component is 0.5 and fundamentalist component is 0.5), neo-liberal agent (n=1, a=0, f=0) and alternative agent (n=0, a=1, f=0)|
|Figure 2. All possible agents' socio-political types that could occur in the simulation. Top: neo-liberal, alternative and fundamentalist agent. Bottom: agents with indeterminate socio-political type|
|Table 2. The calculations of an agent's attitudes toward each issue according to the agent's socio-political type|
|Table 3: All possible agent attitudes|
|Table 4: Changes in an agent's attitude toward issues dependent on the issue's level of importance|
|level of importance|
|Table 5. All possible communication situations between two agents and resulting agent types|
|Figure 3. Three examples of the potential outcomes of inter-agent communication. Top: a communication where both agents retain their initial socio-political types. Middle: a neo-liberal agent influences the second agent and, as a result, the second agent changes its type to neo-liberal. Bottom: both agents' types have changed as a consequence of inter-agent communication|
|Figure 4. Khepera robot/agent and sensors used in the simulation (lines represent the range of sensors that measure distance to objects)|
|Table 6: All possible agent behaviours in the simulation|
|-1||turn||immediately turn around from issue|
|-0.5||slow turn||turn around from issue|
|0.5||around||stay around issue|
|NO ISSUE DETECTED||random||random moving|
|Figure 5. Issue robot/agent and sensors used for an issue agent (lines represent sensors and their range)|
|Table 7: Strings emitted by an issue depending on its level of importance|
level of importance
level of importance
|Redistribution of wealth||'red'||'RED'|
|Figure 6. Simulation world in the initial configuration. There are nine agents (shown in black) in the arena, and eight issues, each with a different colour. Agents and issues' are randomly distributed. The diagram in the middle shows agents' and issues sensor ranges and positions. The right diagram shows a 3D view of the world|
|Table 8. Number of all different simulation runs (400) carried out in this work|
|Table 9. All different experimental situations (80) studied in this work|
|Figure 7. Left: screenshot of the simulation at the start. Middle and right: screenshots at the end of two different simulation runs|
The sum of all agents' physical distances from their closest issue in the arena was measured for every simulation step. We calculated a value D as an indicator of the agents' spatial convergence towards the issues during the run. Its degree of fluctuation shows how smoothly the agents converge.
|D - Sum of all agents' distance from their closest issue. |
di - Distance of agent number 'i' from its closest issue.
N - Number of agents (N=9).
|Figure 8. Convergence value D for two different agent positions with two different issue positions (communication off, active issues). Average value graph (left) and standard error graph (right) are shown|
|Figure 9. Convergence value D for two different agent positions with two different issue positions (communication on, active issues). Average value graph (left) and standard error graph (right) are shown|
|Table 10. All different experimental situations studied here. For every different experimental situation (cell entry in the table), 20 independent simulations were run|
|Figure 10. Comparison of all situations with passive issues and active issues. Top: average convergence D graph, bottom: standard error of D graph|
|Figure 11. Comparison of all situations with inter-agent communication off and on. Top: average convergence D graph, bottom: standard error of D graph|
|Figure 12. Comparison of all experimental situations with different initial agents' types. Top: average convergence D graph, bottom: standard error of D graph|
|Figure 13. Comparison of average number of neo-liberal (top), alternative (middle) and fundamentalist (bottom) agents at the start and at the end of the simulation for experimental situations with different initial agent types|
|Figure 14. Comparison of average number of agents with indeterminate socio-political types at the start and at the end of simulation for experimental situations with different initial agent types|
|Figure 15. Comparison of average number of agents with determinate (neo-liberal, alternative and fundamentalist) and indeterminate socio-political types at the end of simulation for experimental situations with different initial agent types|
2 For examples of Webots simulations please refer to the Webots web site at www.cyberbotics.com.
3 The Khepera robot is widely used in AI research community. It is small, practical and designed for research and education. The Webots simulation package is equipped with full support for Khepera robots. For more details, please refer to the Khepera web site at www.k-team.com.
4 During test simulation runs issues' and agents' emitter ranges were adjusted in order to obtain suitable physical proximity when an agent perceived an issue or another agent (i.e. when the agent's receiver receives an issue's or another agent's signal). Larger ranges result in agents that are communicating although they are physically remote, smaller ranges make the reception of signals unlikely.
5 Test runs have shown that the activation of three issue sensors occurs when two, three or four agents are present around the issue. The exact number depends on the agents' positions in relation to the issue's sensors, but in most cases it represents the presence of three agents.
6 When a scenario contains ten or more agents, the Webots simulation software runs very slowly.
7 During simulation all agents show a tendency to gather around issues towards which they have positive attitudes. In this way aggregations of agents positioned around issues are formed.
8 Standard error values for all data were also calculated.
9 For some references about communication theory models please refer to Hauser (1997), Smith (1977) and Pettersson (1993).
10 Except in the case of the random situation that contained the highest number of agents with indeterminate socio-political type, which shows the worst overall convergence toward issues.
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