Communication is a critical part of enabling multi-agent systems to cooperate. This means that applying formal methods to protocols governing communication within multi-agent systems provides useful confidence in its ...
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ISBN:
(数字)9783030974572
ISBN:
(纸本)9783030974572;9783030974565
Communication is a critical part of enabling multi-agent systems to cooperate. This means that applying formal methods to protocols governing communication within multi-agent systems provides useful confidence in its reliability. In this paper, we describe the formal verification of a complex communication protocol that coordinates agents merging maps of their environment. The protocol was used by the LFC team in the 2019 edition of the multi-agent programming contest (MAPC). Our specification of the protocol is written in Communicating Sequential Processes (CSP), which is a well-suited approach to specifying agent communication protocols due to its focus on concurrent communicating systems. We validate the specification's behaviour using scenarios where the correct behaviour is known, and verify that eventually all the maps have merged.
In this paper we describe our participation in the 2017 edition of the multi-agent programming contest as team 'lampe'. Our strategy was implemented in C++;it uses a centralised organisation of agents and eval...
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In this paper we describe our participation in the 2017 edition of the multi-agent programming contest as team 'lampe'. Our strategy was implemented in C++;it uses a centralised organisation of agents and evaluates different strategies based on an internal simulation of the future game state. Strategies are generated using handwritten heuristics in a randomised fashion, also relying on the internal simulation.
This paper describes the team BusyBeaver, that participated in and won the multi-agent programming contest 2017. Its strategy is based on dividing agents into three static groups modeling the work chain of buying, ass...
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This paper describes the team BusyBeaver, that participated in and won the multi-agent programming contest 2017. Its strategy is based on dividing agents into three static groups modeling the work chain of buying, assembling and delivering items. The team is coordinated by a centralized agent doing most of the high-level planning, usually using greedy algorithms and specialized heuristics. There is a heavy focus on proactively buying and assembling some items, in order to quickly complete upcoming jobs.
This work focuses on coalition formation among heterogeneous agents for a simulated scenario involving logistic and coordination problems. We investigate whether organising a team of agents into a number of coalitions...
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ISBN:
(纸本)9789897583506
This work focuses on coalition formation among heterogeneous agents for a simulated scenario involving logistic and coordination problems. We investigate whether organising a team of agents into a number of coalitions, in which agents collaborate with each other to achieve particular goals, can increase the effectiveness of the team. We apply coalition structure generation specifically to the 2017 multi-agent programming contest, where the agents controlling various autonomous vehicles form a competing team that has to solve logistic problems simulated on the map of a real city. We experiment on three approaches with different configurations. The first uses only a task-allocation mechanism, while the other approaches use either an optimal or a heuristic coalition formation algorithm. Our results show that coalition formation can improve the performance of a participating team under some circumstances. In particular, coalition formation can indeed play an important role when we aim to balance the skills in groups of agents selected to accomplish some given set of tasks given a larger team of cooperating agents in the presence of dynamically created tasks.
The multi-agent programming contest in 2017 expanded upon the agents in the City scenario used in the 2016 edition of the contest. In the agents in the City contest, two teams compete to accomplish logistic tasks in s...
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The multi-agent programming contest in 2017 expanded upon the agents in the City scenario used in the 2016 edition of the contest. In the agents in the City contest, two teams compete to accomplish logistic tasks in simulations using realistic city maps from OpenStreetMap. The new version of the scenario shifted emphasis to include jobs that require assembled items, new types of facilities, and larger teams, resulting in a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and any new additions from our participation in the previous year. One such new addition, is that now we have fully explored the use of all three programming dimensions (agent, environment, and organization) found in JaCaMo, the multi-agent system development platform that we used to implement our team. We also provide some discussion and analysis on what we believe were some of our most influential matches.
This work focuses on coalition formation among heterogeneous agents for the 2017 multi- agentprogrammingcontest. An agent is a computer system that is capable of independent action to achieve its goals. In order to ...
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This work focuses on coalition formation among heterogeneous agents for the 2017 multi- agentprogrammingcontest. An agent is a computer system that is capable of independent action to achieve its goals. In order to increase the effectiveness of the agents, we can organise them into coalitions, in which the agents collaborate with each other to achieve individual or common goals. We investigate and apply coalition structure generation (the first activity of the coalition formation process) in simulated scenarios, specifically the 2017 contest scenario, where the agents forming a competing team cooperate to solve logistic problems simulated on the map of a real city. In order to achieve our goal, we integrate coalition formation algorithms into the JaCaMo platform by means of a CArtAgO artefact, named CFArtefact. We use the implementation of the SMART-JaCaMo team for experimenting with the coalition formation approach in the contest scenario. We experiment on three approaches in the contest domain with different configurations. In the first, we use only a task- allocation mechanism, while the other approaches use an optimal coalition formation algorithm and a heuristic coalition formation algorithm. We conducted several experiments to compare the advantages of each approach. Our results show that coalition formation algorithms can improve the performance of a participating team when dealing with low job rates (i. e., how quickly new jobs are created by the simulation). However, as we increase the job rate, the approach using only task allocation has better performance. Even a heuristic coalition formation approach has close performance to the optimal one in that case. Coalition formation can play an important role when we aim to balance each group of agents to accomplish some particular goal given a larger team of cooperating agents.
There have been many attempts to integrate automated planning and rational agents. Most of the research focuses on adding support directly within agentprogramming languages, such as those based on the Belief-Desire-I...
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ISBN:
(数字)9783030857394
ISBN:
(纸本)9783030857394;9783030857387
There have been many attempts to integrate automated planning and rational agents. Most of the research focuses on adding support directly within agentprogramming languages, such as those based on the Belief-Desire-Intention model, rather than using off-the-shelf planners. This approach is often believed to improve the computation time, which is a common requirement in real world applications. This paper shows that even in complex scenarios, such as in the multi-agent programming contest with 50 agents and a 4 s deadline for the agents to send actions to the server, it is possible to efficiently integrate agent languages with off-the-shelf automated planners. Based on the experience with this case study, the paper discusses advantages and disadvantages of decoupling the agents from the planners.
We describe the approach used to develop the multi-agent system of herders that competed as the Jason-DTU team at the multi-agent programming contest 2010. We also participated in 2009 with a system developed in the a...
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We describe the approach used to develop the multi-agent system of herders that competed as the Jason-DTU team at the multi-agent programming contest 2010. We also participated in 2009 with a system developed in the agent-oriented programming language Jason which is an extension of agentSpeak. We used the implementation from 2009 as a foundation and therefore much of the work done this year was on improving that implementation. We present a description which includes design and analysis of the system as well as the main features of our agent team strategy. In addition we discuss the technologies used to develop this system as well as our future goals in the area.
This paper describes the team Galoan (it is a local Persian name that means shepherd), one of the participants in the multi-agent programming contest 2010. We present the agents' architecture as well as the team s...
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This paper describes the team Galoan (it is a local Persian name that means shepherd), one of the participants in the multi-agent programming contest 2010. We present the agents' architecture as well as the team strategies and the coordination and cooperation approaches. Our system was developed using the blackboard style and was implemented in Java. In our approach each agent stores its perception in a shared world model which is called blackboard. Coordination has been implemented through a supervisor. The supervisor uses the stored information in the blackboard and coordinates the agents to achieve a goal. Finally, we outline our observations and discuss the performance of our team.
We provide a detailed description of the Jason-DTU system, including the used methodology, tools as well as team strategy. We also discuss the experience gathered in the contest. In spring 2009 the course "Artifi...
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We provide a detailed description of the Jason-DTU system, including the used methodology, tools as well as team strategy. We also discuss the experience gathered in the contest. In spring 2009 the course "Artificial Intelligence and multi-agent Systems" was held for the first time on the Technical University of Denmark (DTU). A part of this course was a short introduction to the multi-agent framework Jason, which is an interpreter for agentSpeak, an agent-oriented programming language. As the final project in this course a solution to the multi-agent programming contest from 2007, the Gold Miners scenario, was implemented. Finally we decided to participate in this year's contest with an implementation made in Jason as well.
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