Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Knowledge Representation and Reasoning using Metalogic in a Cooperative Multiagent Environment

  • Kim, Koono (Global Leadership School, Handong Global University)
  • Received : 2022.06.14
  • Accepted : 2022.07.07
  • Published : 2022.07.29
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Abstract

In this study, it propose a proof theory method for expressing and reasoning knowledge in a multiagent environment. Since this method determines logical results in a mechanical way, it has developed as a core field from early AI research. However, since the proposition cannot always be proved in any set of closed sentences, in order for the logical result to be determinable, the range of expression is limited to the sentence in the form of a clause. In addition, the resolution principle, a simple and strong reasoning rule applicable only to clause-type sentences, is applied. Also, since the proof theory can be expressed as a meta predicate, it can be extended to the metalogic of the proof theory. Metalogic can be superior in terms of practicality and efficiency based on improved expressive power over epistemic logic of model theory. To prove this, the semantic method of epistemic logic and the metalogic method of proof theory are applied to the Muddy Children problem, respectively. As a result, it prove that the method of expressing and reasoning knowledge and common knowledge using metalogic in a cooperative multiagent environment is more efficient.

본 연구에서는 멀티에이전트 환경에서 지식을 표현하고 추론함에 있어서 증명 이론적 방법을 제안한다. 이 방법은 논리적 결과를 기계적 방법으로 결정하므로 초기 인공지능 연구부터 핵심분야로 발전해 왔다. 하지만 임의의 닫힌 문장들의 집합에서 항상 명제가 증명할 수 있지 않기에 논리적 결과가 결정할 수 있어지려면 절 형식의 문장으로 그 표현 범위를 제한한다. 그리고 절 형식의 문장들에서만 적용 가능한, 단순하면서도 강력한 추론 규칙인 비교흡수 원리(Resolution principle)를 적용한다. 또한 증명이론을 메타술어로 표현할 수 있으므로 증명이론의 메타논리로 확장 가능하다. 메타논리가 모델 이론의 인식 논리(epistemic logic)보다 향상된 표현력을 기반으로 실용적인 면과 효율면에서 우월할 수 있다. 이를 입증하기 위해 인식 논리의 의미론과 증명이론의 메타논리 방식으로 각각 Muddy Children 문제에 적용한다. 그 결과 협력적 멀티에이전트 환경에서 메타논리를 사용하여 지식과 공통지식을 표현하고 추론한 방법이 더 효율적임을 증명한다.

Keywords

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