• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.401-412
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• 2008

This paper discusses a design methodology of cooperative path planning for dynamical multi-agent systems with spatial and temporal constraints. The cooperative behavior of the multi-agent systems is specified in terms of the objective function in an optimization formulation. The path of achieving cooperative tasks is then generated by the optimization formulation constructed based on a differential flatness approach. Three scenarios of multi-agent tasking are proposed at the cooperative task planning framework. Given agent dynamics, both spatial and temporal constraints are considered in the path planning. The path planning algorithm first finds trajectory curves in a lower-dimensional space and then parameterizes the curves by a set of B-spline representations. The coefficients of the B-spline curves are further solved by a sequential quadratic programming solver to achieve the optimization objective and satisfy these constraints. Finally, several illustrative examples of cooperative path/task planning are presented.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.46-57
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• 2006

It is well known that mathematical solutions for multi-agent planning problems are very difficult to obtain due to the complexity of mutual interactions among multi-agent. Most of the past research results thus are based on the probabilistic completeness. However, the practicality and effectiveness of the solution from the probabilistic completeness is significantly reduced by heavy computational burden. In this paper, we propose a practically applicable solution technique for multi-agent planning problems, which assures a reasonable computation time and a real world application for more than 3 multi-agents for the case of general shaped paths in agent movement. First, to reduce the computation time, a collision map is utilized for detecting potential collisions and obtaining collision-free solutions for multi-agents. Second, to minimize the maximum of multi-agent task execution time, a method is developed for selecting an optimal priority order. Simulations are finally provided for more than 20 agents to emphasize the effectiveness of the proposed interactive approach to multi-agent planning problems.

• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.211-220
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• 2001

A planning agent supporting customers plays a distinguished role in internet marketplaces. Although several internet marketplaces have been built with the maturity of tools based on internet and distributed technologies, there has been no actual study up to now with respect to the implementation of the planning agent. This paper describes the design and implementation of an XML-based planning agent for internet marketplaces. Since implementing internet marketplaces encounter problems similar to those in other fields such as multidatabase or workflow management systems, we first compare those features. Next we identify functions and roles of the planning agent. The planning agent is implemented using COM+, ASP, and XML, and demonstrated using real data used in an existing system.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.1-10
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• 2004

Advance in Internet technology has changed traditional production planning and control methods. In particular, collaborations between participants in supply chains are being increasingly addressed in industry for enhancing chain-wide productivity. A representative paradigm that emphasizes collaboration in production planning and control is CPFR(Collaborative Planning, Forecasting and Replenishment). In this paper, we present a multi-agent system architecture that supports the collaborations specified in CPFR. The multi-agent system architecture consists of event manager, data view agent, business rule agent, and collaboration agent. The collaboration agent systematically controls negotiation between supplier and buyer with the aid of collaboration protocol and blackboard. The multi-agent system has been implemented with EJB(Enterprise Java Beans).

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.101-114
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• 2018

Several different approaches and mechanisms are introduced to solve the UAV path planning problem. In this paper, we designed and implemented an agent-based simulation software using the Repast platform and Java Genetic Algorithm Package to examine an evolutionary path planning method by implementing and testing within the Repast environment. The paper demonstrates the life-cycle of an agent-based simulation software engineering project while providing a documentation strategy that allows specifying autonomous, adaptive, and interactive software entities in a Multi-Agent System. The study demonstrates how evolutionary path planning can be introduced to improve cognitive agent capabilities within an agent-based simulation environment.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.295-306
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• 2007

It is well known that mathematical solutions for multi-agent planning problems are very difficult to obtain due to the complexity of mutual interactions among multi-agents. Most of the past research results are thus based on the probabilistic completeness. However, the practicality and effectiveness of the solution from the probabilistic completeness is significantly reduced by heavy computational burden. In this paper, we propose a practically applicable solution technique for multi-agent planning problems, which assures a reasonable computation time and a real world application for more than 3 multi-agents, for the case of general shaped paths in agent movement. First, to reduce the computation time, an extended collision map is developed and utilized for detecting potential collisions and obtaining collision-free solutions for multi-agents. Second, a priority for multi-agents is considered for successive and interactive modifications of the agent movements with lower priority. Various solutions using speed reduction and time delay of the relevant agents are investigated and compared in terms of the computation time. A practical implementation is finally provided for three different types of agents to emphasize the effectiveness of the proposed interactive approach to multi-agent planning problems.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1460-1470
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• 2015

This paper presents an approach to solve the long-term generation expansion planning problem of the restructured electricity industry using an agent-based environment. The proposed model simulates the generation investment decisions taken by a particular agent (i.e. a generating company) in a market environment taking into account its competitors’ strategic investment. The investment decision of a particular company is modeled taking into account that such company has imperfect foresight on the future system development hence electricity prices. The delay in the construction of new plants is also explicitly modeled, in order to compute accurately the yearly revenues of each agent. On top of a conventional energy market, several capacity incentive mechanisms including capacity payment and capacity market are simulated, so as to assess their impact on the investment promotion for generation expansion. Results provide insight on the investment cycles as well as dynamic system behavior of long-term generation expansion planning in a competitive electricity industry.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.1-17
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• 2001

This paper deals with an agent-based decision-supporting system for Taguchi experiment planning. Among the four major parts of Taguchi experiment, the planning phase includes the most important decision-making issues such as determination of experiment objectives, quality characteristics, and control factors. The planning phase, however, has not been paid proper attention by experiment designers. In this research, an agent-based decision-supporting system for Taguchi experiment planning has been developed to facilitate the planning tasks of experiment designer. The decision-supporting system is composed of two agent-based mechanisms. The first employs an Internet agent that collects the domain knowledge from knowledge providers who may be distributed in remote places. Another agent then visualizes the collected knowledge and reports it to the experiment designer. Engineers who would normally have difficulties in collaborating because of limitations on their time or because they are in different places can easily work together in the same experiment team and brainstorm to make good decisions. The second agent-based mechanism offers context-sensitive advice generated by another intelligent agent during the experiment planning process. it prevents the experiment designer from making improper decisions, which will increase the feasibility of the experiment and minimize the unnecessary expense of time and resources.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.9-17
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• 2021

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.176-181
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• 2001

Due to the increasing complexity to handle conflicts and interruptions caused by resource failures and rush orders, shop control is obliged to redesign its organization according to the changing demands of the manufacturing control. These demands are leading to the development of decentralization and gradually to their permanent optimization. As a result, a powerful modeling method which can be adapted efficiently is required. The use of agent theory enables specific modeling of the relevant shop planning activities. The planning activities are modeled in a so-called activity modeling through the definition of three classes of agents; Plan Agent, Manufacturing System Agent and Control Agent as well as the description of the cooperative relationship among these agents. On the basis of the activity model the agent-based shop control method is developed which emphasizes the distributed problem-solving and the cooperation with relevant agents.