한국정보과학회논문지:소프트웨어및응용 (Journal of KIISE:Software and Applications)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

지능형 에이전트의 움직이는 장애물 충돌 회피를 위한 베이지안 추론 주도형 행동 네트워크 구조

Bayesian Inference driven Behavior-Network Architecture for Intelligent Agent to Avoid Collision with Moving Obstacles

  • 민현정 (연세대학교 컴퓨터학과) ;
  • 조성배 (연세대학교 컴퓨터산업공학부)
  • 발행 : 2004.08.01
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 변화하는 환경에서 에이전트의 인지 정보로부터 움직이는 물체의 운동모델을 미리 알 수 없는 경우에도 적용할 수 있는 적응적인 행동을 생성하는 방법을 제안한다. 전통적인 에이전트의 지능제어 방법은 환경에 대해 알고 있는 정보를 이용한다는 제약 때문에 강건하지만 다양하고 복잡한 환경에 적용할 수 얼었다. 환경에 대한 정보가 없는 상황에서 에이전트가 자율적으로 행동하기 위해서는 행동 기반의 방법이 적합하며, 실제와 같은 변화는 환경에서 에이전트의 적응적 행동을 위해서는 상황을 미리 추론하고 대처하는 능력이 필요하다. 움직이는 장애물 피하기는 변화하는 환경에서의 적응적 행동생성의 가능성을 보여줄 수 있는 문제이기 때문에 다양한 방법으로 연구되고 있다. 본 논문에서는 고정된 장애물뿐만 아니라 움직이는 장애물을 인지하고 피하는 적응적인 행동을 생성하기 위한 2단계의 제어 구조를 제안한다. 1단계는 상황을 인지하고 자율적으로 행동을 생성하는 행동 네트워크 구조이고 2단계는 변화하는 상황을 추론하고 제어정보를 1단계로 전달하는 베이지안 네트워크 구조이다. 시뮬레이터를 이용한 실험을 통해 제안한 방법으로 고정된 장애물과 움직이는 장애물을 피하고 목적지를 찾아가는 것을 확인할 수 있었다.

This paper presents a technique for an agent to adaptively behave to unforeseen and dynamic circumstances. Since the traditional methods utilized the information about an environment to control intelligent agents, they were robust but could not behave adaptively in a complex and dynamic world. A behavior-based method is suitable for generating adaptive behaviors within environments, but it is necessary to devise a hybrid control architecture that incorporates the capabilities of inference, learning and planning for high-level abstract behaviors. This Paper proposes a 2-level control architecture for generating adaptive behaviors to perceive and avoid dynamic moving obstacles as well as static obstacles. The first level is behavior-network for generating reflexive and autonomous behaviors, and the second level is to infer dynamic situation of agents. Through simulation, it has been confirmed that the agent reaches a goal point while avoiding static and moving obstacles with the proposed method.

키워드

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