• Industrial Engineering and Management Systems
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• 제8권3호
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• pp.181-193
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• 2009

This paper presents a framework for controlling mobile multiple robots connected by communication networks. This framework provides novel methods to control coordinated systems using mobile agents. The combination of the mobile agent and mobile multiple robots opens a new horizon of efficient use of mobile robot resources. Instead of physical movement of multiple robots, mobile software agents can migrate from one robot to another so that they can minimize energy consumption in aggregation. The imaginary application is making "carts," such as found in large airports, intelligent. Travelers pick up carts at designated points but leave them arbitrary places. It is a considerable task to re-collect them. It is, therefore, desirable that intelligent carts (intelligent robots) draw themselves together automatically. Simple implementation may be making each cart has a designated assembly point, and when they are free, automatically return to those points. It is easy to implement, but some carts have to travel very long way back to their own assembly point, even though it is located close to some other assembly points. It consumes too much unnecessary energy so that the carts have to have expensive batteries. In order to ameliorate the situation, we employ mobile software agents to locate robots scattered in a field, e.g. an airport, and make them autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO). ACO is the swarm intelligence-based methods, and a multi-agent system that exploit artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. In this paper, we focus on the implementation of the controlling mechanism of the multi-robots using the mobile agents.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.205-211
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• 2024

Effective warehouse management requires advanced resource planning to optimize profits and space. Robots offer a promising solution, but their effectiveness relies on embedded artificial intelligence. Multi-agent reinforcement learning (MARL) enhances robot intelligence in these environments. This study explores various MARL algorithms using the Multi-Robot Warehouse Environment (RWARE) to determine their suitability for warehouse resource planning. Our findings show that cooperative MARL is essential for effective warehouse management. IA2C outperforms MAA2C and VDA2C on smaller maps, while VDA2C excels on larger maps. IA2C's decentralized approach, focusing on cooperation over collaboration, allows for higher reward collection in smaller environments. However, as map size increases, reward collection decreases due to the need for extensive exploration. This study highlights the importance of selecting the appropriate MARL algorithm based on the specific warehouse environment's requirements and scale.

• 한국게임학회 논문지
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• 제6권3호
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• pp.13-22
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• 2006

오늘날 스포츠, RTS, RPG 게임과 같이 멀티 플레이어가 한 팀을 이루는 집단형 방식의 게임은 팀 인공지능 기술이 더욱 필요하다. 기존의 독립적인 지능형 에이전트는 스스로 문제를 해결하는 자율성 향상 연구에 치중하였으나, 이는 다른 에이전트간의 협동 및 상호작용 능력이 부족하다. 이를 위해 본 논문은 다중에이전트 시스템에서 효과적인 역할 분담과 자율성을 갖는 레벨통합 접근방식을 소개한다. 복잡한 목표를 성취하기 위해 에이전트의 역할 정보를 이용하여 각자의 목표를 할당하고 각 에이전트는 맡은 역할을 동적인 환경에서 스스로 판단하고 행동한다. 팀 전체의 목표는 상호 보완된 역할 분담의 전략적인 측면에서 조정한다. 각 에이전트는 데이터보드를 이용하여 서로의 상태 정보를 공유하여 상호 협동을 유도한다. 역할이 할당된 각 에이전트는 스스로 계획기능을 갖고 있어 동적인 환경에서 적합한 행동을 취한다. 이 협동과 상호작용 과정에서 충돌의 문제점이 발생하는데 이를 제어하는 조정 에이전트의 역할을 전략적 측면에서 접근한다. 본 논문에서 제시하는 레벨통합 접근방식이 기존의 중앙 집권적 접근방식, 분권적 접근방식과 비교 실험하여 기존 방식보다 성능이 향상됨을 보인다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.93.5-93
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• 2002

Distributed Artificial Intelligence (DAI) aims to study and develop techniques that allow interaction among intelligent entities. In the last two decades, some types of DAI architecture have been proposed for various fields. However, it can be noticed that the inconsistency phenomenon has not been dealt with properly. This is probably due to the fact that this phenomenon cannot be handled (at least directly) with classical logic. Hence, to deal with such inconsistencies directly, one should employ a logic other than the classical one. The DAI Architecture described in this work is based on a nonclassical logic called Annotated Paraconsistent Logic.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.879-884
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• 2004

This paper describes the development of a synthetic multi-agent called KMITL Cadence 2003. KMITL Cadence 2003 is a robotic soccer simulation team consisting of eleven autonomous software agents. Each agent operates in a physical soccer simulation model called Robocup Soccer Server which provides fully distributed and real-time multi-agent system environment. All teammates have to cooperate to achieve the common goal of winning the game. The simulation models many aspects of the football field such as noise in ball movements, noisy sensors, unreliable communication channel between teammates and actuators, limited physical abilities and restricted communication. This paper addresses the algorithm to develop the soccer agents to perform basic actions which are scoring, passing ball and blocking the opponents effectively. The result of this development is satisfactory because the successful scoring attempts is increased from 11.1% to 33.3%, successful passing ball attempts is increased from 22.08% to 63.64%, and also, successful intercepting attempts is increased from 88% to 97.73%.

• 한국HCI학회논문지
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• 제14권1호
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• pp.15-22
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• 2019

본 연구에서는 인공지능 대화형 에이전트인 스마트 스피커의 지능형 에이전트로서의 속성, 즉 자율성, 사회성, 반응성, 능동성, 시간연속성, 목표지향성에 대하여 이용자들이 일상적 상호작용을 통하여 어떤 기대를 가지는지, 또한 어떤 기대격차를 갖는지 살펴보고자 하였다. 이를 위해 스마트 스피커 이용자들을 대상으로 반구조화 인터뷰(semi-structured interview)를 진행하고 그라운드 이론에 기반하여 분석하였다. 연구 결과 사람들은 기술수준의 한계로 인해 스마트 스피커의 사회성이나 인간다움에 대해 큰 기대격차를 갖고 있었다. 스마트 스피커의 반응성에 대해서는 긍정적인 기대격차를 갖는 것으로 드러났고, 시간연속적으로 정보를 기억하는 것에 대해서는 정보의 민감성 정도나 제시방식에 따라 양가적 기대격차가 나타났다. 자율적인 추천에 대해서는 낮은 기대수준이 나타났고 능동적인 말걸기에 대해서는 맥락에 맞는 경우에만 선호하는 것으로 나타났다. 본 연구는 스마트 스피커와 상호작용하는 방식을 설계하고 기대 수준을 관리하는데 있어서 함의점을 제시한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.690-693
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• 2003

The purpose of two player game is that a player beats an enemy. In order to win to various enemies, a learning of various strategies is indispensable. However, the optimal action to overcome the enemies will change when the game done over and again because the enemy's actions also change dynamically. Sol it is din-cult that the player aquires the optimal action and that the specific player keeps winning to various enemies. Species who have a competition relation and affect other's existence is called a coevolution. Coevolution has recently attracred considerable interest in the community of Artificial Life and Evolutionary Computation(1). In this paper, we apply Classifier System for agent team to two player game. A reward and a penalty are given to the used rules when the agent achieve specific action in the game and each team's rulebase are evaluated based on the ranking in the league. We show that all teams can acquire the optimal actions by coevolution.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 춘계 종합학술대회 논문집
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• pp.64-67
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• 2006

현재의 컴퓨터 성능과 주변기기 발전은 유비쿼터스 컴퓨팅이라는 새로운 형태의 개념을 도입하게 만들었다. 언제 어디에서라는 유비쿼터스의 개념처럼 네트워크(유 무선)를 이용하여 사용자가 원하는 정보를 장소와 시간에 제약받지 않고 얻음으로서, 사용자들은 정보에 대한 접근이 용이해졌고 방대한 정보를 얻을 수 있게 되었다. 이때 정보의 제공과 피드백은 상호작용성을 가진 에이전트 형태로 구현되는 추세이다. 인공지능 기법을 적용한 지능형 에이전트의 경우 친근감 있는 캐릭터를 사용하여 정보의 신뢰를 향상시킬 뿐만 아니라, 사용자의 행동양식과 의도 파악을 위한 도구로 사용되고 있다. 본 논문은 캐릭터성을 추가한 지능형 에이전트의 개념과 정의를 살펴보고 이를 이용해 수익을 창출하려는 국내 시장의 사례를 연구하여 특징을 분석하고, 성장 가능성과 발전 방안을 모색해보고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.236-240
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• 2000

A cellular automaton is well-known for self-organizing and dynamic behaviors in the field of artificial life. This paper addresses a new neuronic architecture called an evolvable cellular classifier which evolves with the genetic rules (chromosomes) in the non-uniform cellular automata. An evolvable cellular classifier is primarily based on cellular programing, but its mechanism is simpler because it utilizes only mutations for the main genetic operators and resembles the Hopfield network. Therefore, the desirable hi t-patterns could be obtained through evolutionary processes for just one individual agent. As a result, an evolvable hardware is derived which is applicable to classification of bit-string information.

• Asia pacific journal of information systems
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• 제8권3호
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.