Abstract
We present an approach for recognition and subsequent prediction of spatio-temporal patterns in a physical real-time environment. The motivation is to provide a domain-independent approach for the analysis of agent's behavior in adversarial multi-agent scenarios. The goal is to create an opponent- specific model, which is used for behavior prediction. We develop a framework for representing a set of hierarchically structured facts, events and actions using temporal logic. Recognition, learning, and prediction is performed using a probabilistic approach utilizing Bayesian Networks. The system is applied to the domain of the RoboCup 3D Simulation League and evaluated with regard to the recognition-, prediction- and realtime capabilities.
| Original language | English (US) |
|---|---|
| Title of host publication | Frontiers in Artificial Intelligence and Applications |
| Publisher | IOS Press |
| Pages | 979-980 |
| Number of pages | 2 |
| Volume | 215 |
| ISBN (Print) | 9781607506058 |
| DOIs | |
| State | Published - 2010 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Volume | 215 |
| ISSN (Print) | 09226389 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Behavior-analysis and -prediction for agents in real-time and dynamic adversarial environments. / Rachuy, Carsten; Visser, Ubbo E.
Frontiers in Artificial Intelligence and Applications. Vol. 215 IOS Press, 2010. p. 979-980 (Frontiers in Artificial Intelligence and Applications; Vol. 215).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Behavior-analysis and -prediction for agents in real-time and dynamic adversarial environments
AU - Rachuy, Carsten
AU - Visser, Ubbo E
PY - 2010
Y1 - 2010
N2 - We present an approach for recognition and subsequent prediction of spatio-temporal patterns in a physical real-time environment. The motivation is to provide a domain-independent approach for the analysis of agent's behavior in adversarial multi-agent scenarios. The goal is to create an opponent- specific model, which is used for behavior prediction. We develop a framework for representing a set of hierarchically structured facts, events and actions using temporal logic. Recognition, learning, and prediction is performed using a probabilistic approach utilizing Bayesian Networks. The system is applied to the domain of the RoboCup 3D Simulation League and evaluated with regard to the recognition-, prediction- and realtime capabilities.
AB - We present an approach for recognition and subsequent prediction of spatio-temporal patterns in a physical real-time environment. The motivation is to provide a domain-independent approach for the analysis of agent's behavior in adversarial multi-agent scenarios. The goal is to create an opponent- specific model, which is used for behavior prediction. We develop a framework for representing a set of hierarchically structured facts, events and actions using temporal logic. Recognition, learning, and prediction is performed using a probabilistic approach utilizing Bayesian Networks. The system is applied to the domain of the RoboCup 3D Simulation League and evaluated with regard to the recognition-, prediction- and realtime capabilities.
UR - http://www.scopus.com/inward/record.url?scp=77956015192&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956015192&partnerID=8YFLogxK
U2 - 10.3233/978-1-60750-606-5-979
DO - 10.3233/978-1-60750-606-5-979
M3 - Conference contribution
AN - SCOPUS:77956015192
SN - 9781607506058
VL - 215
T3 - Frontiers in Artificial Intelligence and Applications
SP - 979
EP - 980
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
ER -