• Journal of Korea Game Society
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• v.10 no.5
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• pp.95-102
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• 2010

This paper proposes a parallel algorithm of the flocking behaviors using GPU. To do this, we used CUDA as the parallel processing architecture of GPU and then analyzed its characteristics and constraints. Based on them, the paper improved the performance by parallelizing to find the neighbors for an agent which requires the largest cost in the flocking behaviors. We implemented the proposed algorithm on GTX 285 GPU and compared experimentally its performance with the original spatial partitioning method. The results of the comparison showed that the proposed algorithm outperformed the original method up to 9 times with respect to the execution time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.176-180
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• 1998

We may gaze at some peculiar scenes of flocking of birds and fishes. This paper demonstrates that multiple agent mobile robots show complex behaviors from efficient and strategic rules. The simulated flock are realized by a distributed behavioral model and each mobile robot decides its own motion as an individual which moves constantly by sensing the dynamic environment.

• Journal of Korea Game Society
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• v.11 no.5
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• pp.23-30
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• 2011

Flocking behaviors are frequently used in games and computer graphics for realistic simulation of massive crowds. Since simulation of massive crowds in real time is a computationally intensive task, there were many researches on efficient algorithm. In this paper, we find experimentally the fact that there are unnecessary computations in the previous efficient flocking algorithm, and propose a noble algorithm that overcomes the weakness of the previous algorithm with a simple heuristic. A number of experiments were conducted to evaluate the performance of the proposed algorithm. The experimental results showed that the proposed algorithm outperformed the previous efficient algorithm by about 21% on average.

• Journal of Korea Game Society
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• v.9 no.2
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• pp.115-123
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• 2009

This paper proposes an algorithm to improve the performance of the spatial partitioning method for flocking behaviors. The core concept is to improve the performance by using the fact that even if a moving entity, boid in flock continuously changes its direction and position, its k-nearest neighbors, kNN to effect on decision of the next direction is not changed frequently. From the previous kNN, the method to check whether new kNN is changed or not is proposed in this paper and then the correctness of the proposed method is proved by two theorems. The proposed algorithm was implemented and its performance was compared with the conventional spatial partitioning method. The results of the comparison show that the proposed algorithm outperforms the conventional one by about 30% with respect to the number of frames per a second.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.555-561
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• 2014

Artificial intelligence in the game is mainly used to determine patterns of behavior of NPC (Non Player Character) and the enemy, path finding. These artificial intelligence is implemented by FSM (Finite State Machine) or Flocking method. The number of NPC behavior in FSM method is limited by the number of FSM states. If the number of states is too small, then NPC player can know the behavior patterns easily. On the other hand, too many implementation cases make it complicated. The NPC behaviors in Flocking method are determined by the leader's decision. Therefore, players can know easily direction of movement patterns or attack pattern of NPCs. To overcome these problem, this paper proposes agonistic behaviors(attacks, threats, showing courtesy, avoidance, submission)in animals to apply for the NPC, and implements agonistic behaviors using Unity3D engine. This paper can help developing a real sense of the NPC artificial intelligence.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.164-169
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• 1998

Complex "lifelike" behaviors are composed of local interactions of individuals under fundamental rules of artificial life. In this paper, fundamental rules for cooperative group behaviors, "flocking" and "arrangement", of multiple autonomous mobile robots are represented by a small number of fuzzy rules. Fuzzy rules in Sugeno type and their related paramenters are automatically generated from clustering input-output data obtained from the algorithms the group behaviors. Simulations demonstrate the fuzzy rules successfully realize group intelligence of mobile robots.

• Journal of Korea Game Society
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• v.8 no.3
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• pp.87-95
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• 2008

This paper proposes an algorithm for efficient behaviors of boids which freely move and have no predefined position. By finding the kNN and computing the value of behavioral characteristic of a boid approximately, the proposed algorithm improves the conventional spatial partitioning one. To do this, this paper defines and uses the representative boid which has the average direction and position for a group of boids. The proposed algorithm was implemented and compared with the conventional one experimentally. The results of the experimental comparisons show that the proposed algorithm outperforms the conventional one about $-5{\sim}130%$ in terms of the ratio of the number of rendering frames per the second.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.519-522
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• 2005

This paper presents a self-organizing scheme for multi-agent swarm systems based on coupled nonlinear oscillators (CNOs). In this scheme, unicycle robots self-organize to flock and arrange group formation through attractive and repulsive forces among themselves. It is also shown how localized distributed controls are utilized throughout group behaviors such as formation and migration. In the paper, the proposed formation ensures safe separation and good cohesion performance among the robots. Several examples show that the proposed method for group formation performs the group behaviors such as reference path following, obstacle avoidance and flocking, and the formation characteristics such as flexibility and scalability, effectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.222-229
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• 2005

This paper presents a self-organizing scheme for multi-agent swarm systems based on coupled nonlinear oscillators (CNOs). In this scheme, unicycle robots self-organize to flock and arrange group formation through attractive and repulsive forces among themselves. The main result is the maintenance of flexible and scalable formation. It is also shown how localized distributed controls are utilized throughout group behaviors such as formation and migration. In the paper, the proposed formation ensures safe separation and good cohesion performance among the robots. Several examples show that the proposed method for group formation performs the group behaviors such as reference path following, obstacle avoidance and flocking, and the formation characteristics such as flexibility and scalability, effectively.

• Journal of the Korean Mathematical Society
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• v.61 no.3
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• pp.565-602
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• 2024

We study the collective behaviors of two second-order nonlinear consensus models with a bonding force, namely the Kuramoto model and the Cucker-Smale model with inter-particle bonding force. The proposed models contain feedback control terms which induce collision avoidance and emergent consensus dynamics in a suitable framework. Through the cooperative interplays between feedback controls, initial state configuration tends to an ordered configuration asymptotically under suitable frameworks which are formulated in terms of system parameters and initial configurations. For a two-particle system on the real line, we show that the relative state tends to the preassigned value asymptotically, and we also provide several numerical examples to analyze the possible nonlinear dynamics of the proposed models, and compare them with analytical results.