Modeling of Structural Adjustment Processes of Farming Enterprises: The Need for Implementation of Cooperation and Collaboration Strategies

The research objective is to shed light on the structural adjustment process of farming enterprises in Switzerland. To this end, an analytical tool is developed which allows us to identify the most important influencing factors and to estimate in advance their effects on the structural adjustment process. The resulting structures will be compared with the relevant goals of the (agricultural) policy in order to draw conclusions about the effectiveness of the adjustment process. Using scenario techniques combined with an agent-based simulation, the influencing factors are varied to investigate alternative options like cooperation and collaboration strategies for improving the effectiveness with respect to the social, ecological and economic goals.Agent-based simulation, structural change, cooperation strategies, collaboration, Agricultural Finance, C61, Q12, Q15,

The Influence of Multi-agent Cooperation on the Efficiency of Taxi Dispatching

The paper deals with the problem of the optimal collaboration scheme in taxi dispatching between customers, taxi drivers and the dispatcher. The authors propose three strategies that differ by the amount of information exchanged between agents and the intensity of cooperation between taxi drivers and the dispatcher. The strategies are evaluated by means of a microscopic multi-agent transport simulator (MATSim) coupled with a dynamic vehicle routing optimizer (DVRP Optimizer), which allows to realistically simulate dynamic taxi services as one of several different transport means, all embedded into a realistic environment. The evaluation is carried out on a scenario of the Polish city of Mielec. The results obtained prove that the cooperation between the dispatcher and taxi drivers is of the utmost importance, while the customer–dispatcher communication may be reduced to minimum and compensated by the use of more sophisticated dispatching strategies, thereby not affecting the quality of service

A Novel Cooperation and Competition Strategy Among Multi-Agent Crawlers

Multi-Agent theory which is used for communication and collaboration among focused crawlers has been proved that it can improve the precision of returned result significantly. In this paper, we proposed a new organizational structure of multi-agent for focused crawlers, in which the agents were divided into three categories, namely F-Agent (Facilitator-Agent), As-Agent (Assistance-Agent) and C-Agent (Crawler-Agent). They worked on their own responsibilities and cooperated mutually to complete a common task of web crawling. In our proposed architecture of focused crawlers based on multi-agent system, we emphasized discussing the collaborative process among multiple agents. To control the cooperation among agents, we proposed a negotiation protocol based on the contract net protocol and achieved the collaboration model of focused crawlers based on multi-agent by JADE. At last, the comparative experiment results showed that our focused crawlers had higher precision and efficiency than other crawlers using the algorithms with breadth-first, best-first, etc

Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams

Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existing state-of-the-art methods in MARL often recover slowly -- if at all -- from such malfunctions once agents have already converged on a cooperation strategy. To address this gap, we present the Collaborative Adaptation (CA) framework. CA introduces a mechanism that guides collaboration and accelerates adaptation from unforeseen failures by leveraging inter-agent relationships. Our findings demonstrate that CA enables agents to act on the knowledge of inter-agent relations, recovering from unforeseen agent failures and selecting appropriate cooperative strategies.Comment: Presented at Multi-Agent Dynamic Games (MADGames) workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

An agent-based architecture for managing the provision of community care - the INCA (Intelligent Community Alarm) experience

Community Care is an area that requires extensive cooperation between independent agencies, each of which needs to meet its own objectives and targets. None are engaged solely in the delivery of community care, and need to integrate the service with their other responsibilities in a coherent and efficient manner. Agent technology provides the means by which effective cooperation can take place without compromising the essential security of both the client and the agencies involved as the appropriate set of responses can be generated through negotiation between the parties without the need for access to the main information repositories that would be necessary with conventional collaboration models. The autonomous nature of agents also means that a variety of agents can cooperate together with various local capabilities, so long as they conform to the relevant messaging requirements. This allows a variety of agents, with capabilities tailored to the carers to which they are attached to be developed so that cost-effective solutions can be provided. </p

Exploring Cooperation Between Secondary Agricultural Educators and Livestock Extension Agents: A Case Study

Due to the common goal of youth leadership development, there is the opportunity for Cooperative Extension\u27s 4-H clubs and Agricultural Education\u27s FFA chapters to be more effective through cooperation. The qualitative study discussed here used focus groups to explore the level of and perceptions regarding cooperation among agricultural educators and Extension agents. Major themes that positively influenced cooperation were identified as: the relationship between the agricultural educator and Extension agent, the awareness of the other profession, and the understanding and perceptions of cooperation. Findings of this study indicated a lack of collaboration between disciplines

Active collaboration in relative observation for Multi-agent visual SLAM based on Deep Q Network

This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair,abstract representation of agents in MAS are learned in the process of collaboration among agents. Finally, each agent must act with a certain degree of freedom according to MAS-DQN. The simulation results of comparative experiments prove that this mechanism improves the efficiency of cooperation in the process of multi-agent SLAM

Evolutionary Games defined at the Network Mesoscale: The Public Goods game

The evolutionary dynamics of the Public Goods game addresses the emergence of cooperation within groups of individuals. However, the Public Goods game on large populations of interconnected individuals has been usually modeled without any knowledge about their group structure. In this paper, by focusing on collaboration networks, we show that it is possible to include the mesoscopic information about the structure of the real groups by means of a bipartite graph. We compare the results with the projected (coauthor) and the original bipartite graphs and show that cooperation is enhanced by the mesoscopic structure contained. We conclude by analyzing the influence of the size of the groups in the evolutionary success of cooperation.Comment: 10 pages, 5 figure

Breaking Up a Research Consortium

Inter-firm R&D collaborations through contractual arrangements have become increasingly popular, but in many cases they are broken up without any joint discovery. We provide a rationale for the breakup date in R&D collaboration agreements. More specifically, we consider a research consortium initiated by a firm A with a firm B. B has private information about whether it is committed to the project or a free-rider. We show that under fairly general conditions, a breakup date in the contract is a (secondbest) optimal screening device for firm A to screen out free-riders. With the additional constraint of renegotiation proofness, A can only partially screen out free-riders: entry by some free-riders makes sure that A does not have an incentive to renegotiate the contract ex post. We also propose empirical strategies for identifying the three likely causes of a breakup date: adverse selection, moral hazard, and project non-viability