Details
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 60-67 |
| Number of pages | 8 |
| ISBN (electronic) | 9781538632338 |
| Publication status | Published - 23 Oct 2017 |
| Externally published | Yes |
| Event | 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017 - New York, United States Duration: 22 Aug 2017 → 25 Aug 2017 |
Publication series
| Name | 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017 |
|---|
Abstract
In this paper we discuss our recent approach for evolving a diverse set of agents for both the Pac-Man and the Ghost Team track of the current Ms. Pac-Man vs. Ghost Team competition. We used genetic programming for generating various agents, which were distributed in multiple populations. The optimization includes cooperative and adversarial subtasks, such that Pac-Man is constantly competing against the Ghost Team, whereas the Ghost Team is formed of four cooperatively evolving populations. For the generation of a Ghost Team and calculation of the associated fitness we took one individual from each population. This strict separation preserves the evolution pressure for each population such that respective Ghost Teams compete against each other in developing an efficient cooperation in catching Pac-Man. This approach not only is useful for developing a versatile set of playing agents, but also for adapting the team to the current behavior of the competing populations. Ultimately, we aim for optimizing both tasks in parallel.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Media Technology
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2017 IEEE Conference on Computational Intelligence and Games, CIG 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 60-67 8080416 (2017 IEEE Conference on Computational Intelligence and Games, CIG 2017).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Combining cooperative and adversarial coevolution in the context of pac-man
AU - Dockhorn, Alexander
AU - Kruse, Rudolf
PY - 2017/10/23
Y1 - 2017/10/23
N2 - In this paper we discuss our recent approach for evolving a diverse set of agents for both the Pac-Man and the Ghost Team track of the current Ms. Pac-Man vs. Ghost Team competition. We used genetic programming for generating various agents, which were distributed in multiple populations. The optimization includes cooperative and adversarial subtasks, such that Pac-Man is constantly competing against the Ghost Team, whereas the Ghost Team is formed of four cooperatively evolving populations. For the generation of a Ghost Team and calculation of the associated fitness we took one individual from each population. This strict separation preserves the evolution pressure for each population such that respective Ghost Teams compete against each other in developing an efficient cooperation in catching Pac-Man. This approach not only is useful for developing a versatile set of playing agents, but also for adapting the team to the current behavior of the competing populations. Ultimately, we aim for optimizing both tasks in parallel.
AB - In this paper we discuss our recent approach for evolving a diverse set of agents for both the Pac-Man and the Ghost Team track of the current Ms. Pac-Man vs. Ghost Team competition. We used genetic programming for generating various agents, which were distributed in multiple populations. The optimization includes cooperative and adversarial subtasks, such that Pac-Man is constantly competing against the Ghost Team, whereas the Ghost Team is formed of four cooperatively evolving populations. For the generation of a Ghost Team and calculation of the associated fitness we took one individual from each population. This strict separation preserves the evolution pressure for each population such that respective Ghost Teams compete against each other in developing an efficient cooperation in catching Pac-Man. This approach not only is useful for developing a versatile set of playing agents, but also for adapting the team to the current behavior of the competing populations. Ultimately, we aim for optimizing both tasks in parallel.
UR - http://www.scopus.com/inward/record.url?scp=85039978813&partnerID=8YFLogxK
U2 - 10.1109/CIG.2017.8080416
DO - 10.1109/CIG.2017.8080416
M3 - Conference contribution
AN - SCOPUS:85039978813
T3 - 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017
SP - 60
EP - 67
BT - 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017
Y2 - 22 August 2017 through 25 August 2017
ER -