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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Incremental elaboration of scenario-based specifications and behavior models using implied scenarios
ACM Transactions on Software Engineering and Methodology (TOSEM)
Behavior Modeling Using a Hierarchical HMM Approach
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
2005 Special Issue: Efficient streaming text clustering
Neural Networks - 2005 Special issue: IJCNN 2005
Approximate Processing of Massive Continuous Quantile Queries over High-Speed Data Streams
IEEE Transactions on Knowledge and Data Engineering
A modeling language for vehicle motion control behavioral specification
Proceedings of the 2006 international workshop on Software engineering for automotive systems
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approaches for modeling individuals within organizational simulations
WSC '04 Proceedings of the 36th conference on Winter simulation
An approach to human behavior modeling in an Air Force simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Tree Based Behavior Monitoring for Adaptive Fraud Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Mining evolving data streams for frequent patterns
Pattern Recognition
Mining association rules in very large clustered domains
Information Systems
Topic sentiment mixture: modeling facets and opinions in weblogs
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Design Synthesis from Interaction and State-Based Specifications
IEEE Transactions on Software Engineering
A decision network framework for the behavioral animation of virtual humans
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Multiobjective clustering with automatic k-determination for large-scale data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Shape-and-Behavior Encoded Tracking of Bee Dances
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using simulation to predict market behavior for outbound call centers
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Intelligent agents for moving and operating computer generated forces
Proceedings of the 2007 Summer Computer Simulation Conference
On elliptical model for forest fire spread modeling and simulation
Mathematics and Computers in Simulation
Human decision-making behavior and modeling effects
Decision Support Systems
Cluster Kernels: Resource-Aware Kernel Density Estimators over Streaming Data
IEEE Transactions on Knowledge and Data Engineering
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining class-bridge rules based on rough sets
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Abstract: Effective prediction for fighters behaviors is crucial for air-combats as well as for many other game fields. In this paper, we present three patterns to predict the behaviors of fighters that are the ActionStreams pattern, the Owner_Actions pattern and the Time_Owner_Actions pattern, where: (1) ActionStreams pattern is a coarse granular for describing the fighter's behaviors with action identifier whereas without distinguishing the time and the executor/owner; (2) Owner_Actions pattern is a finer granular for describing the fighter's behaviors with the action identifier and the executor whereas without distinguishing the time; and (3) Time_Owner_Actions pattern encapsulates the action identifier, the time, and also the executor. Based on such fighters' behaviors patterns, we explore the data structures used to store and the satisfied properties used to mine; and further, by designing and implementing the relevant mining/processing algorithms and systems, we have discovered some experience patterns of the fighters' behaviors and have conducted certain valid predictions for the fighters' behaviors. We also present the experimental results conducted on the simulation platform of the air to air combats. The results show that our method is effective.